Advances In Speech Recognition Research Articles

Voice Assistant for Blind Person

This paper presents a comprehensive study on the design, implementation, and evaluation of a voice assistant system tailored specifically for visually impaired individuals. With the proliferation of voice-enabled devices, there exists a promising opportunity to empower visually impaired users through natural language interaction. Our proposed system incorporates advanced speech recognition, natural language understanding, and synthesis techniques to provide intuitive and efficient communication channels. Additionally, we address the unique challenges faced by visually impaired users, such as contextual understanding, accessibility, and usability. Through user studies and evaluations, we demonstrate the efficacy and usability of the proposed system, highlighting its potential to significantly enhance the independence and quality of life for visually impaired individuals in various daily tasks and activities

Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English

The advent of speech recognition algorithms has opened new avenues for enhancing pronunciation accuracy in the teaching of spoken English. As English continues to be a global lingua franca, proficiency in spoken communication holds paramount importance for individuals across diverse professional and academic domains. This paper introduces Forward Backward Recognition Deep Learning (FBRDL), a novel approach aimed at leveraging speech recognition algorithms to enhance pronunciation accuracy in the teaching of spoken English. FBRDL incorporates advanced deep learning techniques such as recurrent neural networks (RNNs) and transformers, which excel in modeling sequential data and capturing long-range dependencies. By leveraging these powerful architectures, FBRDL can effectively handle the inherent variability and complexity of speech signals, enabling robust and accurate recognition even in noisy or adverse environments. Moreover, FBRDL is characterized by its adaptability and scalability, making it well-suited for a wide range of applications across industries. Whether in the realm of virtual assistants, automatic transcription, or voice-controlled devices, FBRDL offers a versatile solution capable of meeting the demands of modern speech recognition tasks. FBRDL integrates principles from deep learning with advanced speech recognition techniques to provide learners with real-time feedback and guidance on their pronunciation. By analyzing spoken English inputs and identifying phonetic discrepancies, FBRDL offers targeted interventions tailored to individual learners' needs. FBRDL achieves an average increase in pronunciation accuracy of 20% compared to traditional teaching methods. Moreover, qualitative assessments underscore the effectiveness of FBRDL in facilitating more precise and efficient acquisition of spoken English skills.

Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology

The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening up new possibilities for cross-cultural communication and interaction. This paper introduces a Statistical Phase-based Bilingual Speech (SPBS) system designed to facilitate seamless language translation and dialogue between multiple languages, with a focus on Chinese and English. Leveraging advanced machine learning models and techniques, such as Recurrent Neural Networks (RNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, the SPBS system achieves high translation accuracy, computational efficiency, and fluency of translations. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB. Furthermore, the paper presents the machine learning settings and training progress of the SPBS system, demonstrating its effectiveness in accurately classifying and translating speech inputs across languages. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB.

Let’s Talk Games: An Expert Exploration of Speech Interaction with NPCs

Recent years have witnessed significant advances in speech recognition and language processing technologies, enabling natural language conversations with computers. Concurrently, the gaming industry seeks to heighten immersion as one of the leading mediums for entertainment. This work investigates the potential and challenges of using speech interaction in single-player video games, particularly for interactions with NPCs. We conducted an online survey with video game experts ( N = 20 ) alongside in-depth interviews with researchers specializing in conversational user interfaces and game user research ( N = 16 ). Our findings emphasize experts’ recognition of the considerable potential of speech interaction in games, fostering increased immersion, engagement, and entertainment. Additionally, experts address pertinent concerns like privacy issues and play environment limitations. Drawing from our findings, we provide practical recommendations for integrating speech interaction in single-player games. These encompass potential benefits, challenges, accessibility, and social implications. We further address potential regulatory requirements and offer implementation tips to enhance player experience.

Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation.

This research presents the development of a cutting-edge real-time multilingual speech recognition and speaker diarization system that leverages OpenAI's Whisper model. The system specifically addresses the challenges of automatic speech recognition (ASR) and speaker diarization (SD) in dynamic, multispeaker environments, with a focus on accurately processing Mandarin speech with Taiwanese accents and managing frequent speaker switches. Traditional speech recognition systems often fall short in such complex multilingual and multispeaker contexts, particularly in SD. This study, therefore, integrates advanced speech recognition with speaker diarization techniques optimized for real-time applications. These optimizations include handling model outputs efficiently and incorporating speaker embedding technology. The system was evaluated using data from Taiwanese talk shows and political commentary programs, featuring 46 diverse speakers. The results showed a promising word diarization error rate (WDER) of 2.68% in two-speaker scenarios and 11.65% in three-speaker scenarios, with an overall WDER of 6.96%. This performance is comparable to that of non-real-time baseline models, highlighting the system's ability to adapt to various complex conversational dynamics, a significant advancement in the field of real-time multilingual speech processing.

Screen Time and Parent-Child Talk When Children Are Aged 12 to 36 Months

Growing up in a language-rich home environment is important for children's language development in the early years. The concept of "technoference" (technology-based interference) suggests that screen time may be interfering with opportunities for talk and interactions between parent and child; however, limited longitudinal evidence exists exploring this association. To investigate the longitudinal association between screen time and 3 measures of parent-child talk (adult words, child vocalizations, and conversational turns) when children are 12 to 36 months of age. This Australian prospective cohort study used advanced speech recognition technology to capture young children's screen time and home language environment, on an average 16-hour day. Data were collected from 220 families once every 6 months in the family home when children were 12, 18, 24, 30, and 36 months of age, from January 1, 2018, to December 31, 2021. Statistical analysis took place from November 1, 2022, to July 31, 2023. Language Environment Analysis (LENA) technology provided automated counts of children's language environment and exposure to electronic noise. The exposure of interest was screen time, which was calculated based on manual coding of LENA electronic noise audio segments. Three measures of parent-child talk were outcomes of focus: adult words, child vocalizations, and conversational turns. Separate models were run for each of the 3 outcomes and included adjustment for child sex, child age, maternal educational level, number of children at home, number of home activities, and primary caregiver's psychological distress. The study included 220 families (120 girls [54.6%]; mean [SD] gestational age of children, 39.3 [1.5] weeks; mean [SD] age of mother at childbirth, 31.3 [4.8] years). Adjusted linear mixed-effect models demonstrated that increases in screen time were associated with decreases in measures of parent-child talk. The largest decreases were seen at 36 months, when an additional minute of screen time was associated with a reduction of 6.6 (95% CI, -11.7 to -1.5) adult words, 4.9 (95% CI, -6.1 to -3.7) child vocalizations, and 1.1 (95% CI, -1.4 to -0.8) conversational turns. Findings of this study support the notion of technoference for Australian families, whereby young children's exposure to screen time is interfering with opportunities to talk and interact in their home environment. This finding has implications for interventions and supports aimed at promoting a language-rich home environment, with families needing support in understanding the potential association of screen time with opportunities for children and adults to talk and interact in their home environment.

Speech Recognition Enhancement and Compression Perception in Russian Translation Teaching Cooperative System Application

Abstract In the digital age, characterized by the ubiquity of big data and artificial intelligence, Russian translation education is poised for transformative change. This study explores the integration of advanced speech recognition and compressed perception technologies into Russian translation instruction, highlighting their potential to elevate educational quality and translator training. Utilizing the Spring Boot framework, we developed a comprehensive Russian translation teaching system, incorporating a Russian-Chinese speech translation model that leverages the RNN Transducer model and an N-gram language model, alongside compressed perception theory for efficient speech signal processing. Our findings reveal a notable enhancement in Russian speech recognition accuracy and processing speed, with a 4.73% improvement in endto-end speech detection accuracy and a significant reduction in word error rate, outperforming CTC and CTC-LM models by 14.79% to 24.85%. Furthermore, speech quality and intelligibility scores experienced marked improvements. The application of speech recognition enhancement and compressed perception technologies emerges as a vital strategy for enriching Russian translation pedagogy and fostering the development of proficient translators.

Deep Neural Network-based Mixed Speech Recognition Technology for Chinese and English

In the field of human-computer interaction, the current more advanced speech recognition systems are all single speech recognition, and it is urgent to adopt new in-depth learning technology to improve the existing speech recognition system. In this context, this research is based on DNN and investigates mixed speech recognition techniques for both Chinese and English. A single speech recognition algorithm based on DNN is first investigated, and then a new hybrid Chinese and English speech recognition model is constructed by fusing the attention mechanism and CTC loss function. In the construction of the hybrid speech recognition model, the end-to-end model and Transformer framework are used to combine the monotonic alignment property of the CTC loss function, which allows complex sound units to be transformed into characters for easy extraction and recognition. The performance of the constructed models was tested on Chinese speech dataset, English speech dataset and mixed Chinese and English speech dataset to determine the recognition accuracy and speed of the models. The results show that the proposed recognition model achieves 81.2% recognition accuracy and 100 recognition speed/minute on the Chinese-English mixed speech dataset, which is much better than the other three models. This study successfully addresses the need for improved speech recognition systems by introducing a novel hybrid model for mixed Chinese-English speech recognition. The experimental results confirm the superiority of the proposed model, achieving high accuracy and rapid recognition speed. The developed model holds promising potential for enhancing human-computer interaction and enabling efficient communication between Chinese and English speakers.

Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype

This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.

Computational Approaches to Phonology: Advances in Speech Recognition and Synthesis

This article explores the field of computational approaches to phonology, with a particular focus on recent increases in popularity and speech synthesis. Phonology, as a department of linguistics, deals with the observation of sounds and their work in language. Recent improvements in this area have resulted in major improvements in accuracy and efficiency, towards the integration of deep recognition strategies together with recurrent neural networks (RNNs) and convolutional neural networks (CNNs). the models have tested the ability to capture complex phonetic patterns and decorate the overall performance of computerized speech reputation structures. These advances have seen packages in multiple domains, such as virtual assistants, text-to-speech structures, and language acquisition tools. This article looks at the main computational procedures used in phonology, along with characteristic-based representations, rule-based modes, and statistical methods. It explores demanding situations related to phonological evaluation, which include phoneme segmentation, pronunciation variants, and language-specific problems. In addition, this paper discusses mixing phonological knowledge with device learning strategies, which enables the development of robust and correct speech recognition and synthesis systems. As usual, this research represents remarkable advances from computational methods to phonology, particularly in the geographical area of speech recognition and synthesis. the combination of technology and linguistic insights has increased the accuracy, naturalness, and efficiency in processing spoken language. These enhancements open up new opportunities to enhance human-pc interaction, language recognition, and many other packages that rely on speech-based technologies.

Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device

Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.

Improved Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition

The recent advancements in conversational Artificial Intelligence (AI) are fastly getting integrated with every realm of human lives. Conversational agents who can learn, understand human languages and mimic the human thinking process have already created a revolution in human lifestyle. Understanding the intention of a speaker from his natural speech is a significant step in conversational AI. A major challenge that hinders the efficacy of this process is the lack of language resources. In this research, we address this issue and develop a domain-specific speech command classification system for the Sinhala language, one of the low-resourced languages. An effective speech command classification system can be utilized in several value added applications such as speech dialog systems. Our speech command classification system is developed by integrating Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). The ASR engine is implemented using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and it converts a Sinhala speech command into a corresponding text representation. The text classifier, which is implemented as an ensemble unit of several classifiers, predicts the intent of the speaker when provided with the above text output. In this paper, we discuss and evaluate various algorithms and techniques that can be utilized to optimize the performance of both the ASR and text classifier. As well, we present our novel Sinhala speech data corpus of 4.15[Formula: see text]h which is based on the banking domain. As the final outcome, our system reports its Sinhala speech command classification accuracy as 91.03%. It shows that our system outperforms the state-of-the-art speech-to-intent mapping systems developed for the Sinhala language. The individual evaluation on the ASR system reports a 9.91% Word Error Rate and a 19.95% Sentence Error Rate, suggesting the applicability of advanced speech recognition techniques despite the limited language resources. Finally, our findings deliver useful insights on further research in speech command classification in the low-resourced context.

A Survey of Recommender Systems Based on Deep Learning

In recent years, deep learning’s revolutionary advances in speech recognition, image analysis, and natural language processing have gained significant attention. Deep learning technology has become a hotspot research field in the artificial intelligence and has been applied into recommender system. In contrast to traditional recommendation models, deep learning is able to effectively capture the non-linear and non-trivial user-item relationships and enables the codification of more complex abstractions as data representations in the higher layers. In this paper, we provide a comprehensive review of the related research contents of deep learning-based recommender systems. First, we introduce the basic terminologies and the background concepts of recommender systems and deep learning technology. Second, we describe the main current research on deep learning-based recommender systems. Third, we provide the possible research directions of deep learning-based recommender systems in the future. Finally, concludes this paper.

Initial Progress Toward Development of a Voice-Based Computer-Delivered Motivational Intervention for Heavy Drinking College Students: An Experimental Study.

BackgroundComputer-delivered interventions have been shown to be effective in reducing alcohol consumption in heavy drinking college students. However, these computer-delivered interventions rely on mouse, keyboard, or touchscreen responses for interactions between the users and the computer-delivered intervention. The principles of motivational interviewing suggest that in-person interventions may be effective, in part, because they encourage individuals to think through and speak aloud their motivations for changing a health behavior, which current computer-delivered interventions do not allow.ObjectiveThe objective of this study was to take the initial steps toward development of a voice-based computer-delivered intervention that can ask open-ended questions and respond appropriately to users’ verbal responses, more closely mirroring a human-delivered motivational intervention.MethodsWe developed (1) a voice-based computer-delivered intervention that was run by a human controller and that allowed participants to speak their responses to scripted prompts delivered by speech generation software and (2) a text-based computer-delivered intervention that relied on the mouse, keyboard, and computer screen for all interactions. We randomized 60 heavy drinking college students to interact with the voice-based computer-delivered intervention and 30 to interact with the text-based computer-delivered intervention and compared their ratings of the systems as well as their motivation to change drinking and their drinking behavior at 1-month follow-up.ResultsParticipants reported that the voice-based computer-delivered intervention engaged positively with them in the session and delivered content in a manner consistent with motivational interviewing principles. At 1-month follow-up, participants in the voice-based computer-delivered intervention condition reported significant decreases in quantity, frequency, and problems associated with drinking, and increased perceived importance of changing drinking behaviors. In comparison to the text-based computer-delivered intervention condition, those assigned to voice-based computer-delivered intervention reported significantly fewer alcohol-related problems at the 1-month follow-up (incident rate ratio 0.60, 95% CI 0.44-0.83, P=.002). The conditions did not differ significantly on perceived importance of changing drinking or on measures of drinking quantity and frequency of heavy drinking.ConclusionsResults indicate that it is feasible to construct a series of open-ended questions and a bank of responses and follow-up prompts that can be used in a future fully automated voice-based computer-delivered intervention that may mirror more closely human-delivered motivational interventions to reduce drinking. Such efforts will require using advanced speech recognition capabilities and machine-learning approaches to train a program to mirror the decisions made by human controllers in the voice-based computer-delivered intervention used in this study. In addition, future studies should examine enhancements that can increase the perceived warmth and empathy of voice-based computer-delivered intervention, possibly through greater personalization, improvements in the speech generation software, and embodying the computer-delivered intervention in a physical form.

Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics edited by AmyNeustein, ed. New York: Springer Science and Business Media, 2010.

Anthropology of Work ReviewVolume 34, Issue 1 p. 57-58 Book and Video Review Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics edited by Amy Neustein, ed. New York: Springer Science and Business Media, 2010. Jessica Zimmer, Jessica Zimmer jessicazimmer@hotmail.com Search for more papers by this author Jessica Zimmer, Jessica Zimmer jessicazimmer@hotmail.com Search for more papers by this author First published: 18 June 2013 https://doi.org/10.1111/awr.12007_4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume34, Issue1July 2013Pages 57-58 RelatedInformation

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processing.

Trends and advances in speech recognition

One of the earliest successful applications of machine-learning techniques to pattern recognition was the application of information-theoretic principles to speech recognition. Previous approaches relied heavily on expert input through the painstaking analysis of data to relate speech signals to the word sequences that produced them. Such methodologies were completely displaced by casting the speech recognition problem in a probabilistic framework by modeling the joint probability distribution of speech signals and word sequences. At the beginning of the 21st century, the amount of data and computation to train and build models has increased exponentially, and the emergence of new machine-learning algorithms and methodologies has opened new vistas in approaching complex pattern recognition problems. This is enabled by a new set of machine-learning techniques referred to as graphical models, with computationally tractable training algorithms. Closely related are neural-network modeling techniques, and there has been a resurgence of interest in the application of neural-network concepts, such as deep networks to speech recognition. The explosion of data has caused the development of new ways to capture the key features in massive amounts of data using efficient methods deploying exemplar-based sparse representations. Lastly, all of these different approaches can be tied together in a principled fashion using another variation of graphical models: an exponential model framework. This paper describes the current state of the art in speech recognition systems and highlights the developments that are expected to produce major breakthroughs in our ability to automatically recognize speech using computers.

U-Green City 구현을 위한 상황인지기반 지능형 음성인식 시스템

Green IT based u-City means that u-City having Green IT concept. If we adopt the situation awareness or not, the processing of Green IT may be reduced. For example, if we recognize a lot of speech sound on CCTV in u-City environment, it takes a lot of processing time and cost. However, if we want recognize emergency sound on CCTV, it takes a few reduced processing cost. So, for detecting emergency state dynamically through CCTV, we propose our advanced speech recognition system. For the purpose of that, we adopt HMM (Hidden Markov Model) for feature extraction. Also, we adopt Wiener filter technique for noise elimination in many information coming from on CCTV in u-City environment.

CCTV 응급상황에 따른 지능형 음성인식 시스템 구현

For the emergency detecting in general CCTV environment of our daily life, the monitoring by only images through CCTV information occurs some problems especially in cost as well as man power. Therefore, in this paper, for detecting emergency state dynamically through CCTV as well as resolving some problems, we propose our advanced speech recognition system. For the purpose of it, we adopt HMM(Hidden Markov Model) in our system to do a feature extraction. Also, we adopt Wiener filter technique for noise elimination in many information coming from on CCTV environment. In this paper, our system send only the emergency speech information to a manager to deal with emergency state effectively.

Highly accurate children’s speech recognition for interactive reading tutors using subword units

Speech technology offers great promise in the field of automated literacy and reading tutors for children. In such applications speech recognition can be used to track the reading position of the child, detect oral reading miscues, assessing comprehension of the text being read by estimating if the prosodic structure of the speech is appropriate to the discourse structure of the story, or by engaging the child in interactive dialogs to assess and train comprehension. Despite such promises, speech recognition systems exhibit higher error rates for children due to variabilities in vocal tract length, formant frequency, pronunciation, and grammar. In the context of recognizing speech while children are reading out loud, these problems are compounded by speech production behaviors affected by difficulties in recognizing printed words that cause pauses, repeated syllables and other phenomena. To overcome these challenges, we present advances in speech recognition that improve accuracy and modeling capability in the context of an interactive literacy tutor for children. Specifically, this paper focuses on a novel set of speech recognition techniques which can be applied to improve oral reading recognition. First, we demonstrate that speech recognition error rates for interactive read aloud can be reduced by more than 50% through a combination of advances in both statistical language and acoustic modeling. Next, we propose extending our baseline system by introducing a novel token-passing search architecture targeting subword unit based speech recognition. The proposed subword unit based speech recognition framework is shown to provide equivalent accuracy to a whole-word based speech recognizer while enabling detection of oral reading events and finer grained speech analysis during recognition. The efficacy of the approach is demonstrated using data collected from children in grades 3–5, namely 34.6% of partial words with reasonable evidence in the speech signal are detected at a low false alarm rate of 0.5%.