Speech Recognition Research Articles

Speech-mediated manipulation of da Vinci surgical system for continuous surgical flow

AbstractWith the advent of robot-assisted surgery, user-friendly technologies have been applied to the da Vinci surgical system (dVSS), and their efficacy has been validated in worldwide surgical fields. However, further improvements are required to the traditional manipulation methods, which cannot control an endoscope and surgical instruments simultaneously. This study proposes a speech recognition control interface (SRCI) for controlling the endoscope via speech commands while manipulating surgical instruments to replace the traditional method. The usability-focused comparisons of the newly proposed SRCI-based and the traditional manipulation method were conducted based on ISO 9241-11. 20 surgeons and 18 novices evaluated both manipulation methods through the line tracking task (LTT) and sea spike pod task (SSPT). After the tasks, they responded to the globally reliable questionnaires: after-scenario questionnaire (ASQ), system usability scale (SUS), and NASA task load index (TLX). The completion times in the LTT and SSPT using the proposed method were 44.72% and 26.59% respectively less than the traditional method, which shows statistically significant differences (p < 0.001). The overall results of ASQ, SUS, and NASA TLX were positive for the proposed method, especially substantial reductions in the workloads such as physical demands and efforts (p < 0.05). The proposed speech-mediated method can be a candidate suitable for the simultaneous manipulation of an endoscope and surgical instruments in dVSS-used robotic surgery. Therefore, it can replace the traditional method when controlling the endoscope while manipulating the surgical instruments, which contributes to enabling the continuous surgical flow in operations consequentially.

UTran-DSR: a novel transformer-based model using feature enhancement for dysarthric speech recognition

Over the past decade, the prevalence of neurological diseases has significantly risen due to population growth and aging. Individuals suffering from spastic paralysis, brain attack, and idiopathic Parkinson’s disease (PD), among other neurological illnesses, commonly suffer from dysarthria. Early detection and treatment of dysarthria in these patients are essential for effectively managing the progression of their disease. This paper provides UTrans-DSR, a novel encoder-decoder architecture for analyzing Mel-spectrograms (generated from audios) and classifying speech as healthy or dysarthric. Our model employs transformer encoder features based on a hybrid design, which includes the feature enhancement block (FEB) and the vision transformer (ViT) encoders. This combination effectively extracts global and local pixel information regarding localization while optimizing the mel-spectrograms feature extraction process. We keep up with the original class-token grouping sequence in the vision transformer while generating a new equivalent expanding route. More specifically, two unique growing pathways use a deep-supervision approach to increase spatial data recovery and expedite model convergence. We add consecutive residual connections to the system to reduce feature loss while increasing spatial data retrieval. Our technique is based on identifying gaps in mel-spectrograms distinguishing between normal and dysarthric speech. We conducted several experiments on UTrans-DSR using the UA speech and TORGO datasets, and it outperformed the existing top models. The model performed significantly in pixel’s localized and spatial feature extraction, effectively detecting and classifying spectral gaps. The Tran-DSR model outperforms previous research models, achieving an accuracy of 97.75%.

The Role of Hearing Difficulty: Cognitive-Communication and Speech-in-Noise Deficits in Adults Following Mild Traumatic Brain Injury

Purpose: Mild traumatic brain injury (TBI) has a high rate of occurrence across the lifespan. Following mild TBI (mTBI), perceived deficits in cognitive communication and hearing may be noted. This study aimed to determine if adults with a history of mTBI and hearing difficulty perceived greater degrees of cognitive-communication difficulty and performed poorer on a speech-in-noise task when compared to participants with mTBI and controls without reported hearing difficulty. Method: Adults aged 19–77 years with and without a history of mTBI were analyzed. Participants were divided into three groups: control ( n = 49), mTBI ( n = 22), and mTBI with hearing difficulty (mTBI-HD, n = 26). Perception of hearing difficulty was based on a score of ≥ 20 on the Hearing Handicap Inventory or a score ≥ 2 on the Adult Auditory Performance Scale. Self-perceived cognitive communication was measured using the Cognitive Difficulties Scale (CDS) and the La Trobe Communication Questionnaire (LCQ). Speech-in-noise performance was measured using the low-cue speech recognition threshold (SRT) from the Listening in Spatialized Noise–Sentences (LiSN-S) test. Results: Adults in the mTBI-HD group reported significantly greater degrees of cognitive-communication difficulties (higher CDS and LCQ scores) and exhibited significantly poorer speech-in-noise performance on the low-cue LiSN-S condition when compared to the control and mTBI groups. Results also revealed significant correlations between the LiSN-S low-cue SRT and perceived cognitive communication on the CDS. Conclusions: Results of the current study indicate that adults with a history of mTBI and hearing difficulty experience greater subjective cognitive-communication difficulties and perform more poorly on difficult speech-in-noise tasks than adults who do not experience self-perceived hearing difficulties. The results of this study support the consideration of hearing and cognitive-communication difficulties when determining appropriate speech, language, and hearing assessment, treatment, and intervention strategies for individuals following mTBI.

Enhancing linguistic research through AI-powered reference management: A proposal for a voice-controlled academic assistant

In today’s digital age, the amount of available research literature is growing exponentially, making it more challenging for researchers to efficiently manage and cite relevant sources. This issue is particularly pronounced in fields like linguistics, where scholars must contend with interdisciplinary sources spanning linguistics, psychology, and computer science. This paper proposes the development of an AI-powered, voice-controlled academic assistant aimed at enhancing the research experience for linguists by streamlining the literature review and citation process. The assistant would use natural language processing (NLP) and machine learning to allow researchers to search for, retrieve, and cite references using voice commands, thereby eliminating many of the tedious aspects of academic research. This proposal outlines the current state of voice-controlled technology, discusses how these technologies can be implemented in academic workflows, and presents a roadmap for the development of a linguist-friendly reference management system. By addressing key technical, ethical, and practical concerns, this paper offers a compelling vision for the future of linguistic research, powered by AI.

Research of the impact of noise reduction methods on the quality of audio signal recovery

The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

HoLLiECares - Development of a multi-functional robot for professional care.

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g., surgical robots, logistics robots), there are only a few very specialized robotic applications in the care sector. In this work, a multi-functional robot is applied in a hospital, capable of performing activities in the areas of transport and logistics, interactive assistance, and documentation. The service robot platform HoLLiE was further developed, with a focus on implementing innovative solutions for handling non-rigid objects, motion planning for non-holonomic motions with a wheelchair, accompanying and providing haptic support to patients, optical recognition and control of movement exercises, and automated speech recognition. Furthermore, the potential of a robot platform in a nursing context was evaluated by field tests in two hospitals. The results show that a robot can take over or support certain tasks. However, it was noted that robotic tasks should be carefully selected, as robots are not able to provide empathy and affection that are often required in nursing. The remaining challenges still exist in the implementation and interaction of multi-functional capabilities, ensuring ease of use for a complex robotic system, grasping highly heterogeneous objects, and fulfilling formal and infrastructural requirements in healthcare (e.g., safety, security, and data protection).

Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators

BackgroundDebriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants.MethodsWe used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns.ResultsTen debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%.ConclusionOur results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.

FluencyBank Timestamped: An Updated Data Set for Disfluency Detection and Automatic Intended Speech Recognition.

This work introduces updated transcripts, disfluency annotations, and word timings for FluencyBank, which we refer to as FluencyBank Timestamped. This data set will enable the thorough analysis of how speech processing models (such as speech recognition and disfluency detection models) perform when evaluated with typical speech versus speech from people who stutter (PWS). We update the FluencyBank data set, which includes audio recordings from adults who stutter, to explore the robustness of speech processing models. Our update (semi-automated with manual review) includes new transcripts with timestamps and disfluency labels corresponding to each token in the transcript. Our disfluency labels capture typical disfluencies (filled pauses, repetitions, revisions, and partial words), and we explore how speech model performance compares for Switchboard (typical speech) and FluencyBank Timestamped. We present benchmarks for three speech tasks: intended speech recognition, text-based disfluency detection, and audio-based disfluency detection. For the first task, we evaluate how well Whisper performs for intended speech recognition (i.e., transcribing speech without disfluencies). For the next tasks, we evaluate how well a Bidirectional Embedding Representations from Transformers (BERT) text-based model and a Whisper audio-based model perform for disfluency detection. We select these models, BERT and Whisper, as they have shown high accuracies on a broad range of tasks in their language and audio domains, respectively. For the transcription task, we calculate an intended speech word error rate (isWER) between the model's output and the speaker's intended speech (i.e., speech without disfluencies). We find isWER is comparable between Switchboard and FluencyBank Timestamped, but that Whisper transcribes filled pauses and partial words at higher rates in the latter data set. Within FluencyBank Timestamped, isWER increases with stuttering severity. For the disfluency detection tasks, we find the models detect filled pauses, revisions, and partial words relatively well in FluencyBank Timestamped, but performance drops substantially for repetitions because the models are unable to generalize to the different types of repetitions (e.g., multiple repetitions and sound repetitions) from PWS. We hope that FluencyBank Timestamped will allow researchers to explore closing performance gaps between typical speech and speech from PWS. Our analysis shows that there are gaps in speech recognition and disfluency detection performance between typical speech and speech from PWS. We hope that FluencyBank Timestamped will contribute to more advancements in training robust speech processing models.

Hearing What You Can't See: Influence of Face Masks on Speech Perception and Eye Movement by Adults With Hearing Loss.

The aim of the study was to analyze how face masks influence speech perception and time spent looking at the speaker's mouth and eyes by adults with and without hearing loss. Twenty participants with hearing loss and 20 without were asked to repeat Spanish words presented in various conditions, including different types of face masks (no mask, transparent window mask, and opaque mask FFP2) and presentation modes (audiovisual, video only, and audio only). Recognition accuracy and the percentage of time looking at the speaker's eyes and mouth (dwell time) were measured. In the audiovisual condition, participants with hearing loss had significantly better word recognition scores when the speaker wore no mask compared to when they wore an opaque face mask. However, there were no differences between the transparent mask and no mask conditions. For those with typical hearing, the type of face mask did not affect speech recognition. Audiovisual presentation consistently improved speech recognition for participants with hearing loss across all face mask conditions, but for those with typical hearing, it only improved compared to video-only mode. These participants demonstrated a ceiling effect in audiovisual and audio-only modes. Regarding eye movement patterns, participants spent less time looking at the speaker's mouth and more time at the eyes when the speaker wore an opaque mask compared to no mask or a transparent mask. The use of transparent face masks (ClearMask-type model) is recommended in contexts where face masks are still used (hospitals) to prevent the hindering effect of opaque masks (FFP2-type model) in speech perception among people with hearing loss, provided that any fogging of the window of the transparent mask is controlled by wiping it off as needed and the light is in front of the speaker to minimize shadows.

Lived Experience of Postpartum Women in Recovery for Opioid Use Disorder on Medication-Assisted Therapy

Abstract Purpose: The purpose of this study was to understand the lived experiences of postpartum women on medication-assisted therapy for opioid use disorder. Study Design and Methods: van Manen's interpretive hermeneutic phenomenological design was used. Recruitment and data collection from seven participants occurred at an outpatient office offering care for perinatal patients on medication-assisted therapy for opioid use disorder. Data collection occurred between September 2021 and March 2022 via in-person interviews using a demographic data sheet, interview guide, speech recognition software, and digital recordings. Demographic data were analyzed using descriptive statistics. Qualitative data were analyzed using precoding, coding, and thematic analysis via van Manen's interpretive hermeneutic phenomenological method. Results: Seven themes were identified: troubled origins, used opioids to deal with life issues, needed to be self-reliant, opioid use disorder overtook their life, the baby was a motivator to seek treatment, the need for combined medication-assisted therapy and collaborative care, and hope for a better life. Clinical Nursing Implications: Nurses can provide trauma-informed care, support the importance of medication-assisted therapy in recovery, incorporate a collaborative care team with expertise in postpartum opioid use disorder, continue the collaborative care team for a minimum of 1 year, and advocate for the patient's role as parent.

Under-Represented Speech Dataset from Open Data: Case Study on the Romanian Language

This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available.

Разработка и внедрение многопоточного сервиса распознавания речи с помощью связки C#, C++ и Python

<p>This article discusses specificity of multithread industrial systems implementation using Python and its computation libraries. Article contains description of theory, including working principle of the Python’s Global Interpreter Locker (GIL), dependency management, multiprocessing library. The practical part of the article devoted to speech recognition service implementation. The implementation uses interprocess communication through shared memory based on "boost.interprocess" library. The result of integration architecture described by the article is reducing of CPU loading, in that case.</p>

СУЧАСНИЙ ІНТЕРНЕТ-МАРКЕТИНГ З ВИКОРИСТАННЯМ ТЕХНОЛОГІЙ ШТУЧНОГО ІНТЕЛЕКТУ

The article discusses the essence and features of modern Internet marketing based on the use of artificial intelligence (AI) technologies. The article examines how AI can help online marketers collect and analyze consumer data, segment and personalize audiences, create and distribute effective content, optimize and automate advertising campaigns, and measure and improve marketing results. The article also analyzes the latest trends in the use of AI technologies by Ukrainian and foreign companies and identifies their advantages.The investigation identified effective AI tools in online marketing, such as chatbots, recommender systems, text generators, sentiment analysers, face and speech recognition systems, etc. The article presents a comparison of AI tools in online marketing of Ukrainian and foreign companies. Foreign companies use modern products that have no analogues in Ukraine. This is due to the popularity of the trend of using AI technologies in foreign companies. In Ukraine, this trend is developing and we can see it in the use of chatbots and CRM systems. They have been implemented by such companies as PrivatBank and Promodo. Despite this, the development of foreign companies is progressive. Previously, only large corporations could afford to use AI technologies, but now young startups are catching up with the trend. AI combined with human capabilities can actually turn business into a universal tool for profit. However, this path is not without obstacles, so the article highlights the challenges of AI that may affect the economic, legal, ethical, and social spheres of life. Scenarios for preventing possible problems allow for the effective use of AI's potential. The article suggests measures that should help society adapt to changes in the socio-economic environment associated with the displacement of humans by AI. It is important to understand the advantages and disadvantages of modern online marketing tools to ensure effective solutions to business problems.

The effects of noise on objective and subjective measures during speech recognition tasks as stress indicators: preliminary results

Objective indicators of stress associated with annoyance scales are essential tools to advance knowledge in the area. Electrodermal activity (EDA) can be used to evaluate physiological responses, providing a picture of autonomic nervous system. Twenty-five adults were evaluated at three situations of speech recognition tasks with monosyllables: in quiet, competitive noise at 65 dBA and 75 dBA. After each situation with noise, the annoyance scale was applied. After, low-pass filtering was used, and phasic phase was analyzed. Results showed that the percentage of speech recognition hits decreased significantly with noise at 75 dBA (p<0.01), when compared to the situation without noise. Furthermore, it was found that annoyance increased significantly as noise at 75 dBA (p<0.01), when compared to 65 dBA. In relation to EDA, there was a significant increase in the number of peaks when comparing speech recognition situations without noise and with noise at 75 dBA (p<0.01), but not for noise at 65 dBA. Regarding the amplitude of the first skin conductance response peak, there was no significant difference between the three situations. These findings suggest that higher noise levels impact on speech recognition and annoyance, causing greater listening effort and increasing stress levels.

Evaluation of a semi-supervised self-adjustment fine-tuning procedure for hearing aids for asymmetrical hearing loss

Objective This study investigated a previously evaluated self-adjustment procedure with respect to its applicability for asymmetrical hearing loss (AHL). Self-adjusted settings were evaluated for speech recognition in noise and sound preference. Design Participants were given the possibility to adjust the left and right hearing aid separately using a two-dimensional user interface. Two different adjustment sequences were tested. Realistic everyday sound scenes in a laboratory environment were presented. The difference between the ears regarding their speech recognition in noise was tested with two spatial conditions, unaided as well as with the prescriptive formula and the self-adjusted setting. Study sample Nineteen experienced hearing aid users (median age 76 years) with different degrees of AHL were invited to participate in this study. Results Participants adjusted a higher gain slope across frequency in the worse ear than in the better one. The two adjustment sequences resulted in significantly different adjustment durations and gain settings. The difference between the ears regarding speech recognition in noise did not change with the self-adjustment. Overall, group-mean effect sizes were small compared to the parameter space. Conclusions The adjustment procedure can be used also by hearing aid users with AHL to find a possibly preferred gain setting.

Interference of mid-level sound statistics underlie human speech recognition sensitivity in natural noise.

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, it's less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds.

The oleaginous voice: Auto-Tune, linear predictive coding, and the security-petroleum complex

ABSTRACT In the twentieth century, exploration geophysics and digital vocal processing each underwent key technical transformations based on formally similar signal processing techniques derived from prior work in statistics and information theory. Engineers at Texas Instruments (TI) and the Massachusetts Institute of Technology (MIT) developed deconvolution filtering as a way to model geological strata from seismograph recordings in order to facilitate oil exploration. Meanwhile, engineers at Bell Laboratories and Nippon Telegraph and Telephone (NTT) used similar techniques to digitally encode and transmit speech signals more efficiently – a method known as linear predictive coding (LPC). These cognate approaches to modeling geophysical and vocal signals provided a practical basis for subsequent forays by oil industry figures into the world of digital audio. Tracing the histories of four technologies – LPC secure voice transmission terminals, the TI Speak & Spell educational toy, automatic speech recognition, and the vocal pitch correction software Auto-Tune – I analyze the role of security interests in facilitating spillovers between technologies of oil and the voice. I argue that, by approaching both geophysical and vocal signals as matters of national security, the United States’ defense and intelligence services furnished the bulk of socio-economic connective tissue between petroleum geophysics and vocal processing research.

Improvement method for sound quality of pharyngeal speech by using Bayes' theorem

In general, a speech signal can be measured by a microphone and a pharyngeal microphone etc.. However, speech signal measured by a microphone often contains the background noise. On the other hand, though a pharyngeal microphone is effective for background noise, it involves body conducted noise. In this study, we propose an improvement method for sound quality of speech signal measured trough a pharyngeal microphone to achieve well the speech recognition. The relationship between the original speech signal and the speech measured by the pharyngeal microphone is not clear. Therefore, we consider the relationship as multiplicative and additive models of the original speech signal and noise components with unknown parameters. An algorithm to estimate simultaneously the original speech signal and the unknown parameters is proposed by using Bayes' theorem based on the measured speech through the pharyngeal microphone. Finally, a speech recognition experiment is conducted to confirm the effectiveness of the proposed algorithm.

Subjective and functional outcome measures to evaluate performance of pediatric cochlear implant

BackgroundThe majority of research on the effects of cochlear implants has been on clinical evaluations of efficacy (hearing and speech skills and auditory thresholds). But it is unclear how much clinical efficacy metrics (speech, hearing, and language tests) actually demonstrate how beneficial cochlear implants (CI) are in everyday circumstances. In this study, we aim to assess the performance of a child’s current CI with both subjective and functional outcomes measures. Also, we aimed to assess factors affecting these outcomes after CI.MethodsThe study group included 45 cochlear-implanted children, and their chronological age ranges between 4 and 12 years. Children were divided according to their language age into three groups: group 1 (G1) includes15 children with a language age of less than 2 years, group 2 (G2) includes 15 children with a language age of 2 to less than 4 years, and group 3 (G3) includes 15 children with language age 4 to 6 years. Assessment was done in two sessions: one for phoniatric evaluation and another for audiological evaluation.ResultsCI enabled children to develop speech perception in a closed manner evidenced by the early speech perception (ESP) test with pattern perception being the easiest and monosyllabic words being the most difficult and the least to develop. Most children with CI were able to control their voice, to a lesser extent to use speech in meaningful use in social situations and to develop communication strategies in oral situations which were the least to develop, and this was evident by Meaningful Use of Speech Scale. Children with CI were able to acquire language, and their receptive language was better than expressive language, but their language age was below their chronological age.ConclusionThe cochlear implant is an effective rehabilitative tool for the treatment of prelingually deafened children, and this was evident by our results. A hierarchal approach of speech assessment according to the language age of the child and selection of age-appropriate speech perception test is very important to assess cochlear-implanted children Speech recognition had a high degree of variability. Early implantation age and increased duration of implant experience were the most important factors associated with good performance with CI, better speech perception, and better language outcomes.

Prototype of a Virtual Assistant System Integrated with AI

This research focuses on the development of a prototype virtual assistant system integrated with artificial intelligence (AI) using the Rational Unified Process (RUP) method. The system is designed to improve user flexibility and efficiency by allowing interaction through voice commands, removing the need for traditional input devices. Python was selected as the primary programming language due to its robust capabilities in handling AI-driven applications. The system utilizes Whisper API for speech recognition, enabling the virtual assistant to accurately interpret voice inputs. Additionally, the integration of Chat GPT API allows the assistant to process and generate responses in a natural, context-aware manner. The combination of these technologies is expected to enhance user experience by making the system more intuitive and seamless, applicable to both daily tasks and complex business environments. The RUP method, structured into phases such as inception, elaboration, construction, and transition, was applied to ensure that the development process was iterative, flexible, and aligned with user needs. The results indicate that the integration of Whisper API with Chat GPT API significantly improves the quality and accuracy of voice-based interaction, streamlining system operation while minimizing the need for complex graphical interfaces. This research demonstrates the potential of voice-driven AI systems in increasing overall operational efficiency.