Speech Utterances Research Articles

Investigating university students' colloquial speech utterances of English in intercultural settings: a case study in Albanian context

In today's world, the widespread use of English, along with student mobility, democratisation, cultural diversity, and the influence of media—especially the Internet—shapes the language environments of higher education institutions from a broad cultural and international communication perspective. This situation is also evident at the University "Ismail Qemali" in Vlore, Albania. The author of this paper researched how English is utilized by both local and foreign students outside the classroom. To gather data, informal conversations were recorded and systematically analyzed using descriptive approaches, statistical modeling, and conversation analysis methods, involving a corpus of 60 students from different native backgrounds (47 local Albanian students and 13 European students). Qualitative and quantitative data were collected to identify phonetic patterns, their occurrences in colloquial speech, and the reasons behind certain linguistic usages or deviations observed in students' "small talk" in multicultural settings. Some participants noted that they are influenced by English and American cultures and lifestyles to varying degrees. Conversely, other students communicated in their native languages while applying basic speech acts that were typical of their original cultural backgrounds. This behaviour illustrated some inconsistencies in phonetics, grammar, and vocabulary. These linguistic deviations reflect how language embodies individual personalities and cultural backgrounds, as speakers adapt their styles and settings in conversation.

Subword Dictionary Learning and Segmentation for Expanding the Vocabulary of Automatic Speech Recognition in Tamil and Kannada

We present automatic speech recognition (ASR) systems for Tamil and Kannada based on subword modeling to effectively handle unlimited vocabulary arising due to the highly agglutinative nature of the languages. We propose a variant of the byte pair encoding (BPE) algorithm named extended-BPE, and Morfessor tool to segment each word into its subwords. We have effectively incorporated maximum likelihood and Viterbi estimation techniques with weighted finite state transducers framework in these algorithms to learn the subword dictionary from a large text corpus. Using the learned subword dictionary, the words in the transcriptions of the training data are segmented into subwords. We train deep neural network ASR systems that recognize the subword sequence for any given test speech utterance. The output subword sequence is then post-processed using deterministic rules to get the final word sequence. Because of this subword design, the number of words that can be recognized is much larger than the number of words in the training corpus. For Tamil ASR, we use 152 hours of data for training and 65 hours for testing, whereas for Kannada ASR, we use 275 hours for training and 72 hours for testing. Upon experimenting with different combinations of segmentation and estimation techniques, we find that the word error rate (WER) reduces drastically when compared to the baseline word-level ASR, achieving a maximum absolute WER reduction of 6.24% and 6.63% for Tamil and Kannada, respectively. Further, comparing our results with that of an end-to-end ASR model available on Github, we have seen that our subword language models can perform comparable to or exceed that of recent end-to-end ASR models for Kannada and Tamil languages.

Структурные особенности нарративов двуязычных детей в республике Кыргызстан

The article deals with Russian-language narratives produced by bilingual children aged 4–6 year, living in Kyrgyzstan and speaking both Russian and Kyrgyz languages. Children are developing in the situation of «mixed bilingualism», with Russian as a dominant language. Narratives were collected using the MAIN experimental tool, which includes several sets of pictures for telling/retelling stories, standard texts for retellings, and tables for evaluating speaking skills. In the article we consider the features of macro- and microstructure of narratives. The macrostructural characteristics of the text are i.e. the completeness of the story itself, coherence, story logic, verbal expression of the internal states of the characters. All our informants were able to create a complete outline of the story, mentioning all the main episodes and involving all the main characters. The children, however, mainly paid attention to the actually performed, really visible actions of the characters (story elements “attempt” and “result of the action”) and less often expressed verbally internal motives, goals, and intentions. As for the internal states, the children mainly described specific actions that they were able to understand unambiguously (ex., they used the verbs of visual perception, mental actions and speech). They described emotions and physical states more rarely. Characteristics of the microstructure include the length of the text and the utterance, lexical and grammatical correctness of speech, syntactic complexity, and lexical diversity. In the microstructure, no significant differences were revealed between children of different age groups: children aged 5 years old, in general, coped with generating narratives more successfully than children aged 4 and 6 years old. The errors mostly belong to the lexical and grammatical levels of the language system: the most common errors are caused by the use of strategies that facilitate language acquisition and are typical either for both bilingual and monolingual children (strategies of simplification and overgeneralization) or those who acquire a second language (grammatical transfer from the first language).

Recognition and Spectral Analysis of Adi Digits

This article presents a method for automatically recognizing spoken digits in the Adi language using the highly effective open-source voice recognition toolkit, “Kaldi”. The study also includes an analysis of the formant frequencies and spectral characteristics of Adi digits. Adi is a zero-resource indigenous tribal language of Arunachal Pradesh with its roots in Tibeto-Burman. UNESCO's 2009 Atlas of the World's Languages in Danger classifies Adi as an endangered language in India. The research utilizes a modest digit corpus from 42 native Adi speakers, employing Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) features from continuous Adi speech utterances. The study explores both Monophone and Triphone models for digit recognition, with notable improvements in accuracy from 79.14% in the Monophone model to 84.29%, 88.43%, and 90.86% in the Triphone models (Tri1, Tri2, and Tri3). This proposed model holds promise for the development of Adi language speech recognition applications.

Approach to Complementary Feeding and Infant Language Use: An Observational Study.

Emerging research suggests that a more infant-led approach to complementary feeding may confer benefits for child language, but these findings are based on parent report studies. Using an observational approach this study examines whether different complementary feeding experiences relate to infant language exposure and language use. Fifty-eight parents recorded a typical infant mealtime in the home (mean infant age = 14 months, SD = 4.15). Observations were coded to measure the prevalence of infant-led and parent-led feeding using the Family Mealtime Coding Scheme. Caregiver language use (word types and token directed at the child, mean length of utterances in child-directed speech, responsiveness and initiations) and the number of infant vocalisations were coded in ELAN using CHAT conventions and parents completed the MacArthur Communicative Development Inventory short form as a measure of child language. Greater observed infant self-feeding was significantly associated with greater observed exposure to language from caregivers (r = 0.312 percentage of infant self-feeding correlated with caregiver word types directed at the child) and a greater number of infant vocalisations (r = 0.320 percentage of infant self-feeding correlated with number of child vocalisations produced). Structural Equation Modelling showed the relationship between infant self-feeding and infant vocalisations to be significantly mediated by enhanced quality and quantity of caregiver child-directed speech (model fit: χ2 [5] = 5.01, p = 0.415, CFI = 1.00 [NF = 0.98], RMSEA = 0.006). Differences in the approach to complementary feeding may shape infant's experiences in ways that support language exposure and use. Autonomy associated with infant self-feeding may enhance opportunities for social interaction.

LINGUISTIC AND DIDACTIC BASIS FOR THE DEVELOPMENT OF FUTURE DOCTORSʼ SPEAKING COMPETENCY

The article defines the content of the concept of “future doctorʼs speech competence” as an integral quality of a medical student, which is manifested in the general ability and readiness to use language means, the laws of their functioning for the construction and understanding of speech utterances in the context of professional activity of doctors; the linguodidactic principles of development of future doctorsʼ speech competence are substantiated. These include the laws of mother tongue acquisition that contribute to the intensive development of communicative and linguistic skills; general didactic and specific principles that determine the direction in the development of means of developing linguistic skills; forms, methods and techniques that correspond to the main forms of work in higher education; teaching aids, the use of which affects the effectiveness of the content of education. It is noted that the development of future doctorsʼ speech competence can be carried out using the following methods of language teaching: oral presentation by the teacher (narration, explanation, lecture), conversation, work with a textbook, observation and analysis of language phenomena, exercise method, programmed learning and interactive methods. The following interactive learning methods have become valuable for the development of medical students’ language competence: work in small groups, imitation, simulation, discussion, microphone, talk shows, rotating groups. All these methods combine into one whole the activities of the teacher and students, which are aimed at improving language knowledge, developing communicative and speech skills, allow to manage studentsʼ cognitive activity productively and realize educational, educational, developmental, motivational, control and correctional functions of learning in a higher education institution

Intertextuality and interactivity as attributive properties of network creativity

Intertextuality is a concept of postmodernism, meaning the interrelationship of diff erent texts. The fi rst idea close to the idea of intertextuality was outlined by M. Bakhtin, who proved that dialogicity is a property of any statement, but this concept was conceptualized in the works of Y. Kristeva in the 1960s. Currently, intertextuality is considered as any of the possible relationships between texts: quotation, allusion, parody, imitation, etc. Intertextuality, as M. Bakhtin showed, is inherent in any dialogue: between sources of speech utterance; between people using quotes, hints, and statements that are recognizable to both; between written sources, where diff erent texts contain similar or common excerpts, references, and reliance on methodology; between people and texts when the reader recognizes fragments of other texts in a given text, like an inlay or palimpsest (text written on a lost original text). Interactivity is the concept of correlation between subjects or actors of an action, which is expressed in their mutual activity. The author correlates these concepts with the characteristics of network creativity.

Speech Emotion Recognition Using Transfer Learning: Integration of Advanced Speaker Embeddings and Image Recognition Models

Automatic Speech Emotion Recognition (SER) plays a vital role in making human–computer interactions more natural and effective. A significant challenge in SER development is the limited availability of diverse emotional speech datasets, which hinders the application of advanced deep learning models. Transfer learning is a machine learning technique that helps address this issue by utilizing knowledge from pre-trained models to improve performance on a new task in a target domain, even with limited data. This study investigates the use of transfer learning from various pre-trained networks, including speaker embedding models such as d-vector, x-vector, and r-vector, and image classification models like AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and ResNet-50. We also propose enhanced versions of the x-vector and r-vector models incorporating Multi-Head Attention Pooling and Angular Margin Softmax, alongside other architectural improvements. Additionally, reverberation from the Room Impulse Response datasets was added to the speech utterances to diversify and augment the available data. Notably, the enhanced r-vector model achieved classification accuracies of 74.05% Unweighted Accuracy (UA) and 73.68% Weighted Accuracy (WA) on the IEMOCAP dataset, and 80.25% UA and 79.81% WA on the CREMA-D dataset, outperforming the existing state-of-the-art methods. This study shows that using cross-domain transfer learning is beneficial for low-resource emotion recognition. The enhanced models developed in other domains (for non-emotional tasks) can further improve the accuracy of SER.

К ВОПРОСУ О ФОРМИРОВАНИИ ИНОЯЗЫЧНОЙ ТЕКСТОВОЙ КОМПЕТЕНЦИИ

The article is devoted to the characterization of textual competence as a complex linguistic ability integrating language experience in various types of speech activity. As a result of the analysis, the content and structure of the concept of «textual competence» are characterized, the stages of its formation are revealed. Textual competence is considered in the work as an independent competence that is not part of a more general communicative competence. The study emphasizes the mastery of textual competence as a basic elementary cultural skill. The key role of textual competence in the processes of personal socialization and intercultural communication is due to the consideration of the communication process as the implementation of a language system in texts. The foundations of textuality as a set of inherent text parameters are analyzed. In the structure of textual competence, knowledge of the language system, language units and the rules of their combination are highlighted, as well as mechanisms for generating speech utterance, taking into account communicative tasks, due to the nature of the communicative situation. The complex nature of textual competence is revealed, due to the multidimensional nature of the text as a linguistic phenomenon. The research material confirms that the formation of foreign language textual competence in the process of learning a foreign language is aimed not only at developing students’ skills to work with foreign language texts, but also intercultural competence.

Extending speech emotion recognition systems to non-prototypical emotions using mixed-emotion model

In the conventional approach to speech emotion recognition (SER), the classifier is usually trained on acted emotional speech data to predict individual basic emotions. In this work, we extend the SER systems with the realistic assumption of the coexistence of multiple basic emotions in an utterance. We utilize the MSP-Podcast database for developing the SER system, which contains spontaneous speech utterances. From the primary and secondary emotion annotations of this database, we organize six prototypical and seven non-prototypical emotions. We then propose the mixed-emotion model and express the non-prototypical emotions as a linear combination of prototypical emotions. The combination weights of the mixed-emotion model are computed using a normalized dominance scale based algorithm inspired by the integration of basic emotion theory and dimensional emotion theory in human psychology. We first train a prototypical SER model using the ECAPA-TDNN architecture. The softmax predictions from this model serve as emotion profile inputs to the mixed-emotion model, which then predicts the non-prototypical emotions. Assuming the coexistence of multiple emotions, we only apply the utterances with uniform emotion profiles to the mixed-emotion model. The developed system continues with the conventional SER model if the emotion profile tends to delta function owing to the probable occurrence of a single prototypical emotion. We develop the proposed mixed-emotion model based SER framework using MFCC and wav2vec 2.0 extracted features. Further, we show that due to human perception variations, there exist prominent annotation variations in the non-prototypical emotion ground truths. To address that, we extend the supervised evaluation protocols in four different formulations that capture the subjective variability at different levels. The proposed system shows a best-case performance improvement of 7.10% and 8.39% over the conventional prototypical SER model for the MFCC and wav2vec 2.0 features, respectively.

Forming inflection and word-formation operations in preschoolers with mental retardation

Introduction. At the present stage of societal development, great attention is paid to issues affecting the psychological and pedagogical support in the educational process of children with disabilities. One general pattern of dysontogenetic development is violation of speech mediation. The most important areas of assistance to children with special educational needs are: identifying speech defect specifics and creating additional special conditions that will facilitate assimilation of educational programs and the social development of each student’s personality. The study aims to develop and test a program for forming inflection and word-formation operations in preschoolers with mental retardation. Materials and methods. The study involved 92 preschoolers aged 6-7 with mental retardation. The research methods were: Methodology of psycholinguistic study of oral speech disorders in children by R.I. Lalaeva, Diagnostics of oral speech of children aged 4-7 by N.V. Nishcheva; mathematical statistics: χ2-Pearson criterion. Results. The ascertaining experiment showed that preschoolers with mental retardation are characterized by predominantly low level of formation of inflection and word-formation operations. Specific difficulties were identified: omission or incorrect use of prepositions in speech; errors in changing nouns in indirect cases; deficiencies in the agreement of an adjective with a noun in the neuter gender; lexical substitutions of nouns, verbs when forming new words using suffixes and prefixes; replacement and incorrect use of suffixes while forming relative and possessive adjectives, etc. After the formative experiment, significant shifts occurred both in the level of noun inflection operations (χ2 = 19.739; p ≤ 0.05) where the number of children at a low level decreased from 64% to 32%, and in the level of verb word-formation operations (χ2 = 30.61; p ≤ 0.05) where the number of children at an average level increased from 39% to 58%. Conclusion. Preschoolers with mental retardation experience difficulties in grammatically designing speech utterances. The data obtained in the experiment show that speech difficulties are due to the delayed development of inflection and word-formation operations and the qualitative peculiarity of the manifestation of the defect. Studying grammatical operations in young children with mental retardation and the specifics of the manifestation of this disorder in written speech is a promising further direction.

On The “Figurative” Use of the Pronoun “I”

It is generally accepted that the pronoun “I” can only refer to the speaker, and cannot be used in any other way. However, as demonstrated in the article, the “figurative” use of the pronoun “I” is also possible, when a discrepancy between the use of this pronoun and the designation of the speaker may occur. The prgma semantic basis for the possibility of such use is the dual, or two-component reference of the pronoun “I”, which simultaneously indicates the speaker in the hypostasis of the subject of speech (utterance) and as the subject of the sentence. In the most general form, this distinction can be represented as a distinction between performative and descriptive aspects of usage; and this makes it possible to simultaneously carry out semiotically different operations of self- and other- reference. At the same time, in special modal contexts, it is possible to separate these aspects, which may cause polysemy and the figurative (metaphorical) use of the pronoun “I”.

Speechformer-CTC: Sequential modeling of depression detection with speech temporal classification

Speech-based automatic depression detection systems have been extensively explored over the past few years. Typically, each speaker is assigned a single label (Depressive or Non-depressive), and most approaches formulate depression detection as a speech classification task without explicitly considering the non-uniformly distributed depression pattern within segments, leading to low generalizability and robustness across different scenarios. However, depression corpora do not provide fine-grained labels (at the phoneme or word level) which makes the dynamic depression pattern in speech segments harder to track using conventional frameworks. To address this, we propose a novel framework, Speechformer-CTC, to model non-uniformly distributed depression characteristics within segments using a Connectionist Temporal Classification (CTC) objective function without the necessity of input–output alignment. Two novel CTC-label generation policies, namely the Expectation-One-Hot and the HuBERT policies, are proposed and incorporated in objectives on various granularities. Additionally, experiments using Automatic Speech Recognition (ASR) features are conducted to demonstrate the compatibility of the proposed method with content-based features. Our results show that the performance of depression detection, in terms of Macro F1-score, is improved on both DAIC-WOZ (English) and CONVERGE (Mandarin) datasets. On the DAIC-WOZ dataset, the system with HuBERT ASR features and a CTC objective optimized using HuBERT policy for label generation achieves 83.15% F1-score, which is close to state-of-the-art without the need for phoneme-level transcription or data augmentation. On the CONVERGE dataset, using Whisper features with the HuBERT policy improves the F1-score by 9.82% on CONVERGE1 (in-domain test set) and 18.47% on CONVERGE2 (out-of-domain test set). These findings show that depression detection can benefit from modeling non-uniformly distributed depression patterns and the proposed framework can be potentially used to determine significant depressive regions in speech utterances.

Speech Emotion Recognition Incorporating Relative Difficulty and Labeling Reliability.

Emotions in speech are expressed in various ways, and the speech emotion recognition (SER) model may perform poorly on unseen corpora that contain different emotional factors from those expressed in training databases. To construct an SER model robust to unseen corpora, regularization approaches or metric losses have been studied. In this paper, we propose an SER method that incorporates relative difficulty and labeling reliability of each training sample. Inspired by the Proxy-Anchor loss, we propose a novel loss function which gives higher gradients to the samples for which the emotion labels are more difficult to estimate among those in the given minibatch. Since the annotators may label the emotion based on the emotional expression which resides in the conversational context or other modality but is not apparent in the given speech utterance, some of the emotional labels may not be reliable and these unreliable labels may affect the proposed loss function more severely. In this regard, we propose to apply label smoothing for the samples misclassified by a pre-trained SER model. Experimental results showed that the performance of the SER on unseen corpora was improved by adopting the proposed loss function with label smoothing on the misclassified data.

PROSODY OF THE ADDRESSING IN FRENCH POLITICAL SPEECH

Political speech (hereinafter PS) is a monological public speech of a political figure, which relies on a written text, the informative component of which is created at the lexico-grammatical level by the speechwriter, and the task of the politician is to ensure that the information is adequately understood by the listening audience. One way of doing this is to reduce the distance between the speaker and the audience. To achieve this “understanding”, the speaker uses the opportunities provided by the oral channel of communication both at the verbal level - the means of phonetic expression that create the prosody of the address - and at the non-verbal level - from body posture to gestures and eye expressions. In linguistics and psycholinguistics the synergetic interaction between prosody and kinesics is increasingly discussed today. How is this interaction realised in PS and what is its effectiveness? Is it possible to consider the realised PS as a multimodal speech utterance? In order to answer these questions this article examines the current approaches to the study of French PS presented in corpus linguistics [Goldman et al., 2009; Simon et al., 2010, 2013] and interdisciplinary linguistics [Kibrik, 2018; Guaïtella, 2014] and discusses the relevance of their results to describe the prosody of address as a strategic component of PS.

Does Gestural Communication Influence Later Spoken Language Ability in Minimally Verbal Autistic Children?

The current study examined the predictive role of gestures and gesture-speech combinations on later spoken language outcomes in minimally verbal (MV) autistic children enrolled in a blended naturalistic developmental/behavioral intervention (Joint Attention, Symbolic Play, Engagement, and Regulation [JASPER] + Enhanced Milieu Teaching [EMT]). Participants were 50 MV autistic children (40 boys), ages 54-105 months (M = 75.54, SD = 16.45). MV was defined as producing fewer than 20 spontaneous, unique, and socially communicative words. Autism symptom severity (Autism Diagnostic Observation Schedule-Second Edition) and nonverbal cognitive skills (Leiter-R Brief IQ) were assessed at entry. A natural language sample (NLS), a 20-min examiner-child interaction with specified toys, was collected at entry (Week 1) and exit (Week 18) from JASPER + EMT intervention. The NLS was coded for gestures (deictic, conventional, and representational) and gesture-speech combinations (reinforcing, disambiguating, supplementary, other) at entry and spoken language outcomes: speech quantity (rate of speech utterances) and speech quality (number of different words [NDW] and mean length of utterance in words [MLUw]) at exit using European Distributed Corpora Project Linguistic Annotator and Systematic Analysis of Language Transcripts. Controlling for nonverbal IQ and autism symptom severity at entry, rate of gesture-speech combinations (but not gestures alone) at entry was a significant predictor of rate of speech utterances and MLUw at exit. The rate of supplementary gesture-speech combinations, in particular, significantly predicted rate of speech utterances and NDW at exit. These findings highlight the critical importance of gestural communication, particularly gesture-speech (supplementary) combinations in supporting spoken language development in MV autistic children.

Tindak Tutur Ilokusi pada Video Pidato Kemenangan Prabowo-Gibran di Channel YouTube CNBC Indonesia

. This study analyzes the illocutionary speech acts in Prabowo-Gibran's victory speech on YouTube CNBC Indonesia. Using a qualitative descriptive approach, this study examines the speech utterances and identifies the types of illocutionary speech acts used. The results reveal five types of illocutionary speech acts: assertive, directive, declarative, commissive, and expressive. In the speech, there are a variety of utterances that include telling, boasting, begging, forbidding, promising, and thanking. The implication of this research is to broaden the understanding of language use in political communication, as well as to explore the communication strategies used by political leaders in influencing public opinion. This research makes an important contribution to the understanding of modern political dynamics and the interaction between political leaders and the public through digital media.

SUETA: Speaker-specific utterance ensemble based transfer attack on speaker identification system

With the widespread application of speaker identification (SI) systems in security-related tasks, the robustness of SI systems against adversarial examples has garnered increasing attention. Existing works have demonstrated the vulnerability of SI systems to transfer-based black-box attacks, wherein attackers generate adversarial examples with a surrogate model and then transfer them to attack the target system. However, the attack success rate (ASR) of transfer-based black-box attacks is limited by the transferability of adversarial examples. As far as we know, few works have investigated enhancing the transferability of adversarial examples for speech utterances to attack SI systems. Furthermore, existing works only utilize a single utterance in the attack process, but in practical situations, an attacker can usually collect multiple utterances of a speaker. In this paper, we propose a novel transfer-based black-box attack method, called speaker-specific utterance ensemble based transfer attack (SUETA), to attack SI systems. To the best of our knowledge, SUETA is the first transfer-based black-box attack method that utilizes multiple utterances instead of a single one. Furthermore, we also propose an improved variant of SUETA, called SUETA+, by sharing gradients of utterances at the speaker-embedding level. Empirical results show that both SUETA and SUETA+ improve the ASR compared to the baselines under the classical cross-model situation. SUETA+ further shows additional improvement over SUETA, especially in the case of the untargeted attack. SUETA+ also outperforms the baselines in cross-dataset and cross-preprocessor situations, although the ASR for all transfer-based attacks decreases compared to that in the cross-model situation. Moreover, SUETA+ can significantly improve the ASR against commercial APIs (iFlytek and TalentedSoft) and the voice assistant (Tmall Genie) for the untargeted attack compared to the baselines.

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.

A neurocomputational view of the effects of Parkinson's disease on speech production.

The purpose of this article is to review the scientific literature concerning speech in Parkinson's disease (PD) with reference to the DIVA/GODIVA neurocomputational modeling framework. Within this theoretical view, the basal ganglia (BG) contribute to several different aspects of speech motor learning and execution. First, the BG are posited to play a role in the initiation and scaling of speech movements. Within the DIVA/GODIVA framework, initiation and scaling are carried out by initiation map nodes in the supplementary motor area acting in concert with the BG. Reduced support of the initiation map from the BG in PD would result in reduced movement intensity as well as susceptibility to early termination of movement. A second proposed role concerns the learning of common speech sequences, such as phoneme sequences comprising words; this view receives support from the animal literature as well as studies identifying speech sequence learning deficits in PD. Third, the BG may play a role in the temporary buffering and sequencing of longer speech utterances such as phrases during conversational speech. Although the literature does not support a critical role for the BG in representing sequence order (since incorrectly ordered speech is not characteristic of PD), the BG are posited to contribute to the scaling of individual movements in the sequence, including increasing movement intensity for emphatic stress on key words. Therapeutic interventions for PD have inconsistent effects on speech. In contrast to dopaminergic treatments, which typically either leave speech unchanged or lead to minor improvements, deep brain stimulation (DBS) can degrade speech in some cases and improve it in others. However, cases of degradation may be due to unintended stimulation of efferent motor projections to the speech articulators. Findings of spared speech after bilateral pallidotomy appear to indicate that any role played by the BG in adult speech must be supplementary rather than mandatory, with the sequential order of well-learned sequences apparently represented elsewhere (e.g., in cortico-cortical projections).