Speaker Information Research Articles

Survey Paper on Smart Note Taker

The Smart Note Taker is an innovative tool designed to maximize the entire process of note taking. By doing so, it enables users to make quick and effective notes either handwritten or voice-activated. Using handwriting recognition and voice-to-text technology, the digital tool transcribes handwritten or spoken information as editable digital text. With cloudbased storage and real-time synchronization, users will be able to access and manage their notes anywhere, at any time, and on multiple devices, thus ensuring the smooth integration of the platform into the workflows of the user. In addition, multimedia is supported, and users are enabled to add images, audio, and video to their notes. Moreover, the AI-powered features, such as automatic summarization, content relevance, and suggestions, help users increase productivity. The Smart Note Taker might be taken to be prepared for students, for working professionals and visually impaired users. It should, therefore, be most adapted to the needs of modern note-taking in terms of being convenient, flexible, and accessible

Zero-shot voice conversion based on feature disentanglement

Voice conversion (VC) aims to convert the voice from a source speaker to a target speaker without modifying the linguistic content. Zero-shot voice conversion has attracted significant attention in the task of VC because it can achieve conversion for speakers who did not appear during the training stage. Despite the significant progress made by previous methods in zero-shot VC, there is still room for improvement in separating speaker information and content information. In this paper, we propose a zero-shot VC method based on feature disentanglement. The proposed model uses a speaker encoder for extracting speaker embeddings, introduces mixed speaker layer normalization to eliminate residual speaker information in content encoding, and employs adaptive attention weight normalization for conversion. Furthermore, dynamic convolution is introduced to improve speech content modeling while requiring a small number of parameters. The experiments demonstrate that performance of the proposed model is superior to several state-of-the-art models, achieving both high similarity with the target speaker and intelligibility. In addition, the decoding speed of our model is much higher than the existing state-of-the-art models.

Exploring User-Centered Evaluation in a Tertiary-Level, Audio-Describer Training Program

Introduction: Audio description (AD) should satisfy the needs of people who are visually impaired; however, quality assessment in university training programs has rarely been based on feedback provided by AD users. Aiming to help students truly understand the needs of people who are visually impaired and their criteria for AD, this study adopted user-centered evaluation in an AD training program offered by a university in Hong Kong. Method: Three evaluators with visual impairment, representing the views of AD users, were invited to evaluate 29 students’ film clips that included AD and discuss their evaluation criteria. Eleven students participated in focus-group interviews and were asked to comment on AD products and discuss their own evaluation criteria. The evaluation criteria of the two groups were compared qualitatively and quantitatively with the aid of the software NVivo. Results: The results showed discrepancies between the users’ and the trainees’ criteria as well as differences in their focuses when evaluating the AD products. The users’ criteria were more numerous; however, certain criteria were perceived exclusively by the trainees. Discussion: Without sufficient knowledge of users’ needs, AD trainees are inclined to provide redundant information (e.g., easily identifiable film sounds and speaker information) while neglecting some aspects of AD (e.g., the prudent use of pronouns and literary expressions), which are deemed important by the users. Implications for Practitioners: The results prompted AD instructors to integrate user feedback into AD training to improve students’ understanding of how to produce AD that better meets its users’ needs.

Diversity on the American Academy of Orthopaedic Surgeons National Meeting Podium: Changes Over Two Decades.

There has been an increase in diversity initiatives regarding selecting speakers for the American Academy of Orthopaedic Surgeons (AAOS) annual meeting and courses. The purpose of this study was to determine the percentage of female or underrepresented minority (URM) speakers for instructional course lectures (ICLs) and AAOS courses over the past 2 decades including a surrogate for expertise. For 2002, 2012, and 2022, the academic and demographic information of speakers and the number of publications at the time of their speaking role were obtained and compared by sex and URM status. Owing to the unequal sample sizes between male versus female cohorts and URM versus non-URM cohorts, the Welch t -test was used. The percentage of ICL and AAOS course speakers who were female increased over time (ICL, AAOS courses): 2002 (2.6%, 3.3%), 2012 (3.9%, 6.3%), and 2022 (11.8%, 15.5%) ( P < 0.001, P < 0.001). The percentage of female AAOS fellows in these years was 2.9%, 4.7%, and 7.4%, respectively. For ICLs and AAOS course speakers, female presenters had fewer publications than male counterparts (ICL, AAOS courses): 2002 ( P < 0.001, P = 0.048), 2012 ( P = 0.003, P < 0.001), and 2022 ( P < 0.001, P < 0.001). For ICLs in 2022, URM speakers had a similar number of publications compared with non-URM speakers. In 2022, URMs comprised 6.9% of ICL speakers and 4% of AAOS fellows. For 2022 ICLs, there were no significant differences in academic institution, position, or region when compared by sex or URM status. For AAOS courses, the percentage of URM speakers increased over time: 2002 (1.1%), 2012 (4.5%), and 2022 (8.6%). For AAOS courses, URM presenters had similar publications compared with non-URM presenters in 2002 and 2022 but less in 2012 ( P = 0.027). The percentage of women and URMs presenting ICLs and AAOS courses has increased over the past 2 decades and exceeded the percentage they represent in the AAOS by over 50%. The female cohort has fewer publications, on average, than the male cohort for all years evaluated, indicating no institutional bias against female speakers.

Written vs generated text: “naturalness” as a textual and psycholinguistic category

In the context of the development of text generation technologies, the opposition “naturalness − unnaturalness of text” has been transformed into a new dichotomy: “naturalness – artificiality”. The aim of this article is to investigate the phenomenon of naturalness in this context from two perspectives: analyzing the linguistic characteristics of a natural text against a generated (artificial) text and systematizing introspective perceptions of Russian native speaker informants as to what a “natural” text should be like and how it should differ from a generated text. The material for the study was a parallel corpus of film reviews in Russian, consisting of two subcorpora: reviews written by people and those generated by a large language model based on prompts, which are the beginnings of reviews, from the first subcorpus. The following methods were applied for the comparative analysis of the two subcorpora: computer-assisted text processing for calculating the values of 130 metrics of text linguistic complexity, psycholinguistic experiment, expert text analysis, contrastive analysis. As a result, it was determined that from the point of view of their own linguistic characteristics, “natural” texts differ from generated texts mainly by greater flexibility of syntactic structure, allowing both omission or reduction of structures and redundancy, as well as by slightly greater lexical variability. Naturalness as a psycholinguistic category is related to the informants’ autostereotypical ideas about the cognitive characteristics of people as a species. The analysis of texts erroneously attributed by informants (generated, labelled as natural and vice versa) showed that a number of characteristics of this autostereotype are overestimated by informants, while others, in general, correlate with the linguistic specificity of texts from the subcorpus of written reviews. In conclusion, we formulate definitions of naturalness as a textual and psycholinguistic category.

A multi-level multi-task progressive framework based on relational graph convolutional networks for causal emotion entailment

Causal Emotion Entailment (CEE) aims to identify the corresponding causal utterances given a non-neutral target utterance and its conversational history. Previous studies have been devoted to modeling conversational context by infusing emotion and speaker information separately. However, for target utterances with different emotions, the joint distributions of speaker identities and emotion types for corresponding causal utterances are significantly different. In addition, a single non-neutral target utterance may correspond to multiple causal utterances and vice versa (i.e., one-to-many and many-to-one relations). These factors make the corresponding relation between the non-neutral target utterances and causal utterances more diverse and difficult to model, hindering the performance of existing methods. To this end, we design an Utterance-level Interaction Graph (UIG) and a Pair-level Interaction Graph (PIG). The former explicitly captures differences in the joint distribution of speaker identities and emotion types for target utterances with different emotions by utterance-level interactions. The latter models the correlations between utterances resulting from one-to-many and many-to-one relations by pair-level interactions. Furthermore, we propose a Multi-level Multi-task Progressive Framework (MMPF) for the CEE task, which achieves UIG and PIG using Relational Graph Convolutional Networks. Extensive experiments on RECCON-DD datasets demonstrate that MMPF obtains state-of-the-art performance.

X-TF-GridNet: A time–frequency domain target speaker extraction network with adaptive speaker embedding fusion

Target speaker extraction (TSE) which has the capability to directly extract desired speech given enrollment utterances of the target speaker has attracted more and more attention for its potential applications in solving the cocktail-party problem. Despite the considerable progress made by existing time-domain methods, which have become the dominant approach for TSE, these methods often significantly degrade their performance under more realistic conditions. This paper proposes an innovative approach in the time–frequency (T–F) domain, namely X-TF-GridNet, which uses complex spectrum mapping to extract the real and imaginary (RI) components of the target speech. Specifically, the TF-GridNet block was designed to serve as the primary speaker extractor module. Our proposed method boasts two key extensions: first, a U2-Net style network adeptly extracts robust fixed speaker embeddings, which could efficiently capture and represent target speaker information. Second, an adaptive embedding fusion (AEA) mechanism ensures the effective utilization of target speaker information, which makes the backbone extractor focus on the speech of interest. Additionally, we also introduced a multi-task learning framework, comprising two distinct loss functions, to explicitly enhance both the discriminative speaker embeddings for the reference speech and the overall quality of the target speech. We conducted extensive ablation studies and quantitative comparisons against previous TSE methods on both the WSJ0-2mix and its noisy and reverberant counterparts. Our proposed method achieved a commendable SI-SDR of 19.7 dB with a moderate model size on the WSJ0-2mix dataset, and the SI-SDR can be improved to 20.7 dB with a larger model. Experimental results demonstrated that, compared with existing time-domain approaches, our proposed method not only achieved competitive performance across multiple objective metrics but also mitigated speaker confusion errors under more challenging conditions, including various interferences such as noises and reverberation.

Speaker-aware cognitive network with cross-modal attention for multimodal emotion recognition in conversation

Emotion recognition in conversation (ERC) has gained considerable attention owing to its extensive applications in the field of human-computer interaction. However, previous models have had certain limitations in exploring the potential emotional relationships within the conversation due to their inability to fully leverage speaker information. Additionally, information from various modalities such as text, audio, and video can synergistically enhance and supplement the analysis of emotional context within the conversation. Nonetheless, effectively fusing multimodal features to understand the detailed contextual information in the conversation is challenging. This paper proposes a Speaker-Aware Cognitive network with Cross-Modal Attention (SACCMA) for multimodal ERC to effectively leverage multimodal information and speaker information. Our proposed model primarily consists of the modality encoder and the cognitive module. The modality encoder is employed to fuse multimodal feature information from speech, text, and vision using a cross-modal attention mechanism. Subsequently, the fused features and speaker information are separately fed into the cognitive module to enhance the perception of emotions within the dialogue. Compared to seven common baseline methods, our model increased the Accuracy score by 2.71 % and 1.70 % on the IEMOCAP and MELD datasets, respectively. Additionally, the F1 score improved by 2.92 % and 0.70 % for each dataset. Various experiments also demonstrate the effectiveness of our method.

MRMI-TTS: Multi-Reference Audios and Mutual Information Driven Zero-Shot Voice Cloning

Voice cloning in text-to-speech (TTS) is the process of replicating the voice of a target speaker with limited data. Among various voice cloning techniques, this article focuses on zero-shot voice cloning. Although existing TTS models can generate high-quality speech for seen speakers, cloning the voice of an unseen speaker remains a challenging task. The key aspect of zero-shot voice cloning is to obtain a speaker embedding from the target speaker. Previous works have used a speaker encoder to obtain a fixed-size speaker embedding from a single reference audio unsupervised, but they suffer from insufficient speaker information and content information leakage in speaker embedding. To address these issues, this article proposes MRMI-TTS, a FastSpeech2-based framework that uses speaker embedding as a conditioning variable to provide speaker information. The MRMI-TTS extracts speaker embedding and content embedding from multi-reference audios using a speaker encoder and a content encoder. To obtain sufficient speaker information, multi-reference audios are selected based on sentence similarity. The proposed model applies mutual information minimization on the two embeddings to remove entangled information within each embedding. Experiments on the public English dataset VCTK show that our method can improve synthesized speech in terms of both similarity and naturalness, even for unseen speakers. Compared to state-of-the-art reference embedding learned methods, our method achieves the best performance on the zero-shot voice cloning task. Furthermore, we demonstrate that the proposed method has a better capability of maintaining the speaker embedding in different languages. Sample outputs are available on the demo page. 1

Analysis of Deep Generative Model Impact on Feature Extraction and Dimension Reduction for Short Utterance Text-Independent Speaker Verification

Speaker verification is a biometric-based method for individual authentication. However, there are still several challenging problems in achieving high performance in short utterance text-independent conditions, maybe for weak speaker-specific features. Recently, deep learning algorithms have been used extensively in speech processing. This manuscript uses a deep belief network (DBN) as a deep generative method for feature extraction in speaker verification systems. This study aims to show the impact of using the proposed method in various challenging issues, including short utterances, text independence, language variation, and large-scale speaker verification. The proposed DBN uses MFCC as input and tries to extract more efficient features. This new representation of speaker information is evaluated in two popular speaker verification systems: GMM-UBM and i-vector-PLDA methods. The results show that, for the i-vector-PLDA system, the proposed feature decreases the EER considerably from 15.24 to 10.97%. In another experiment, DBN is used to reduce feature dimension and achieves significant results in decreasing computational time and increasing system response speed. In a case study, all the evaluations are performed for 1270 speakers of the NIST SRE2008 dataset. We show deep belief networks can be used in state-of-the-art acoustic modeling methods and more challenging datasets.

What Do Self-Supervised Speech Models Know About Words?

Abstract Many self-supervised speech models (S3Ms) have been introduced over the last few years, improving performance and data efficiency on various speech tasks. However, these empirical successes alone do not give a complete picture of what is learned during pre-training. Recent work has begun analyzing how S3Ms encode certain properties, such as phonetic and speaker information, but we still lack a proper understanding of knowledge encoded at the word level and beyond. In this work, we use lightweight analysis methods to study segment-level linguistic properties—word identity, boundaries, pronunciation, syntactic features, and semantic features—encoded in S3Ms. We present a comparative study of layer-wise representations from ten S3Ms and find that (i) the frame-level representations within each word segment are not all equally informative, and (ii) the pre-training objective and model size heavily influence the accessibility and distribution of linguistic information across layers. We also find that on several tasks—word discrimination, word segmentation, and semantic sentence similarity—S3Ms trained with visual grounding outperform their speech-only counterparts. Finally, our task-based analyses demonstrate improved performance on word segmentation and acoustic word discrimination while using simpler methods than prior work.1

MRSLN: A Multimodal Residual Speaker-LSTM Network to alleviate the over-smoothing issue for Emotion Recognition in Conversation

Multimodal Emotion Recognition in Conversation (ERC) plays a significant role in the field of human–computer intelligent interaction since it enables computers to perceive and infer the emotions expressed by the individuals, thereby intelligently responding to them. Most of current ERC methods pay more attention to modeling the complex interaction between different modalities. However, the features extracted by their unimodal networks are over-smoothed and may contain insufficient intra-speaker contextual information, which results in suboptimal results. In this paper, we focus on the unimodal learning and propose a simple late fusion framework named Multimodal Residual Speaker-LSTM Network (MRSLN), which uses speaker information to directly model inter-speaker and intra-speaker dependency, rather than fuse it into the learned features. MRSLN uses the speaker-LSTM consisting of the inter-speaker LSTM, intra-speaker LSTM, and the residual network between the input and output of the inter-speaker LSTM. Our proposed method can alleviate the issue of over-smoothing in deep Long Short Term Memory (LSTM) network and also incorporate additional intra-speaker contextual information. Extensive experiments conducted on IEMOCAP and MELD datasets demonstrate that MRSLN effectively captures inter-speaker and intra-speaker information and outperforms currently complex state-of-the-art (SOTA) models in efficiency and classification performance.

Dialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue Context

Dialogue generation, as a crucial subtask of dialogue systems, is garnering increasing attention in the field of Natural Language Processing (NLP). The success of dialogue generation relies on effectively utilizing context information to ensure coherent and diverse responses. However, current approaches heavily rely on external sources rather than leveraging the inherent dialogue content. We propose a new approach to address this challenge by introducing semantic segmentation from the field of image processing into NLP. Our contribution lies in the development of a Dialogue Generation model with Hierarchical Encoding and Semantic segmentation of dialogue Context, which is called DGHESC. This model is topic and speaker-aware, capturing the flow of topic and speaker information within the dialogue context using a hierarchical transformer-based framework. Specifically, we extract semantic information at the word-level for each utterance, segment the dialogue context based on topic and speaker semantics, and employ attention mechanisms to model the context at the utterance-level. Experimental results on two open-domain datasets demonstrate the effectiveness of DGHESC. It enhances response quality and achieves state-of-the-art performances on the datasets.

Noise-robust voice conversion using adversarial training with multi-feature decoupling

Most existing voice conversion methods focus primarily on separating speech content from speaker information while overlooking the decoupling of pitch information. Additionally, the quality of converted speech significantly degrades when the speech of the target speaker is contaminated by noises. To address these issues, this paper proposes a noise-robust voice conversion model with multi-feature decoupling based on adversarial training. The proposed framework utilizes three distinct encoders to encode speech content, speaker identity, and pitch information independently, which aims to enhance the performance of decoupling by minimizing their mutual information and reduce the correlations between feature vectors. Moreover, a gradient reversal layer and a noise decoupling discriminator are incorporated into the framework, which extracts noise-resistant speaker representations and content representations through adversarial training to facilitate the synthesis of high-quality speech. In order to optimize the learning process, a training strategy is developed which involves alternating between clean and noisy data during the training of the encoder. This strategy effectively guides and expedites the convergence of the model. Experimental results demonstrate that compared to the state-of-the-art baselines of noise-robust voice conversion, the proposed model achieves improvements around 0.31 and 0.39 in terms of speech naturalness and speaker similarity evaluation metrics, respectively.

Adapting beyond borders: Insights from the 19th Student Council Symposium (SCS2023), the first hybrid ISCB Student Council global event.

The 19th ISCB Student Council Symposium (SCS2023) organized by ISCB-SC adopted a hybrid format for the first time, allowing participants to engage in-person in Lyon, France, and virtually via an interactive online platform. The symposium prioritized inclusivity, featuring on-site sessions, poster presentations, and social activities for in-person attendees, while virtual participants accessed live sessions, interactive Q&A, and a virtual exhibit hall. Attendee statistics revealed a global reach, with Europe as the major contributor. SCS2023's success in bridging in-person and virtual experiences sets a precedent for future events in Computational Biology and Bioinformatics. The details of the symposium, speaker information, schedules, and accepted abstracts, are available in the program booklet (https://doi.org/10.5281/zenodo.8173977). For organizers interested in adopting a similar hybrid model, it would be beneficial to have access to details regarding the online platform used, the types of sessions offered, and the challenges faced. Future iterations of SCS can address these aspects to further enhance accessibility and inclusivity.

Refining Synthesized Speech Using Speaker Information and Phone Masking for Data Augmentation of Speech Recognition

Refining Synthesized Speech Using Speaker Information and Phone Masking for Data Augmentation of Speech Recognition

The Early Detection and Diagnosis of Mental Health Status Employing NLP-Based Methods With ML Classifiers

People now communicate on a variety of internet channels on a daily basis. Natural language processing techniques can be used to deduce users' mental states based on textual or spoken information they post on these sites. Using SMS to predict mental health issues is a proactive step toward better treatment. NLP is transforming the way that professionals in the field of mental health assess patients' freedom of expression in order to identify and diagnose mental illnesses. In addition to offering new avenues for research into human attitudes and behaviors, machine learning techniques can be used to recognize the telltale indications and symptoms of mental illness. In this study, we investigate various supervised classifier methods in depth and use natural language processing (NLP) to identify the mental health state from a text message. People experience suffering from several mental diseases, but the most common ones include PTSD, bipolar disorder, panic disorder, depression, stress, and anxiety. We used Decision Trees, Random Forest, K-Nearest Neighbors, BernoulliNB, and Logistic Regression to classify the data for this investigation. In comparison to the other four classifiers, Logistic Regression performs the best in our suggested strategy. The experimental result confirms that more accurate patient data classification can be achieved with the suggested methodology. With a 93 percent accuracy rate, the suggested model was demonstrated to be efficient.

End-to-end technologies as a tool for developing sociocultural competence among inophones in the process of linguistic and cultural adaptation in the space of “urban text”

Introduction. One of the main processes associated with the stay of an inophone student who came to study a foreign language in a foreign country is linguistic adaptation in a new cultural and geographical space. The result of successful adaptation is the formation of sociocultural competence among the student, awareness of regional identity. From the point of view of the methodology for teaching the Russian language as a foreign language, it seems significant to search for the most effective technologies, methods and techniques that would allow immersing the inophone in the natural environment. End-to-end technologies used in urban space to obtain multidirectional information for native speakers and foreigners meet this methodological task. Authentic texts of chatbots, interactive maps become didactic material for the formation of key competencies of an inophone student.Materials and Methods. The scientific basis of the study is scientific works related to the methodology of teaching the Russian language as a foreign language, the problems of linguistic and cultural adaptation and the use of end-to-end technologies in linguistic education. The research materials are authentic (natural) texts of the digital urban environment: online maps, chat bots, audio guides, which contribute to a more effective linguistic and cultural adaptation of inphones to "alien" space. Based on the methods of analysis and generalization, a theoretical understanding of the experience of working with foreign students at the Nizhny Novgorod State Pedagogical University named after K. Minin. The key concepts used in the work are "digital pedagogy," "end-to-end technologies," "urban text," "regional identity." "sociocultural competence," "linguostranological adaptation."Results. The use of end-to-end technologies in the practice of teaching Russian as a foreign language is currently an effective way to develop communicative competence. Urban space provides an opportunity for direct immersion in the environment of the language being studied. In teaching practice, online cards are actively used, according to which, on the one hand, tasks can be compiled aimed at developing skills in any type of speech activity (reading, speaking, listening, writing), on the other hand, they can become the basis for project activities students. Another online tool for developing communication skills in a foreign language is chatbots. A chatbot makes it possible to learn a foreign language and maintain communication skills at a pace convenient for the student, while eliminating the fear of making mistakes, because there is interaction with artificial intelligence. The AudioGuideNN chatbot allows a student of Russian as a foreign language to enter the urban space, get acquainted with the city and learn the language at the same time.Discussion and Conclusions. Modern end-to-end technologies are effective tools for teaching a foreign language, including Russian as a foreign language. Virtual maps, audio guides, chat bots allow you to relieve the cultural shock of the urban space from a native speaker of a foreign language who is in a new environment for him, create conditions for the linguistic and cultural adaptation of a foreign speaker, and also contribute to the study of the Russian language due to direct immersion in the urban – Russian-language – text.

An analysis of Arabic tautology in English compounds used in customer-service settings in Jordan: A pragmatic perspective

This paper examines the utilization of Arabic tautological expressions, provided by customers as a reiteration of an element of an English compound such as ‘apple pie’ [fatˁi:rit ʔabil bai ‘an apple pie pie’], from the perspective of Relevance Theory (RT). Data was collected from 90 participants, divided into two groups: 40 native speaker informants recruited from various shopping malls, and 50 shop assistants in Amman, Jordan. The first group was interviewed, whereas the second group filled out a multiple-choice task aimed at identifying the type of expression they typically receive when taking orders from customers. The data analysis revealed that Arabic tautological expressions are more frequently used compared to other alternatives. Drawing on RT, we argue that Arabic tautological expressions function as an ostensive stimulus. We propose that tautological expressions, utilized within a code-switching context, function as a means of pragmatic enrichment. This occurs through the process of ad hoc construction, wherein speakers refine the originally encoded concept to convey corrective or contrasting meanings. Instead of lacking information, the use of tautological phrases can be seen as a pragmatic tactic to maximize relevance by minimizing cognitive effort, optimizing successful communication of the intended meaning, and reinforcing contextual effects.

Effects of the landline telephone filter and linguistic context on speaker-dependent variability in /s/

Previous research has demonstrated that vowel formants situated near the lower limit of the bandwidth show effects of the telephone filter (Künzel 2001; Byrne and Foulkes 2004). In this work, the effects of the landline telephone filter are examined for one consonant, namely fricative /s/. Although this speech sound is expected to show large effects of narrowband telephone filters due to its high-frequency spectral characteristics, previous work on Dutch telephone speech showed that, despite its compromised acoustics, /s/ still contains considerable amounts of speaker information (Smorenburg &amp; Heeren, 2021). Using English data that were simultaneously recorded as broadband and telephone speech, this work shows large effects of the telephone filter on both the acoustics and linear discriminant analysis (LDA) speaker classification. Linguistic effects were only observable in studio recordings and generally did not have an effect on the speaker classification with one exception: when the following context is labial, LDA speaker-classification accuracy was higher, indicating idiosyncrasies in anticipatory labial coarticulation.