A new speech corpus of super-elderly Japanese for acoustic modeling

The development of accessible speech recognition technology will allow the elderly to more easily access electronically stored information. However, the necessary level of recognition accuracy for elderly speech has not yet been achieved using conventional speech recognition systems, due to the unique features of the speech of elderly people. To address this problem, we have created a new speech corpus named EARS (Elderly Adults Read Speech), consisting of the recorded read speech of 123 super-elderly Japanese people (average age: 83.1), as a resource for training automated speech recognition models for the elderly. In this study, we investigated the acoustic features of super-elderly Japanese speech using our new speech corpus. In comparison to the speech of less elderly Japanese speakers, we observed a slower speech rate and extended vowel duration for both genders, a slight increase in fundamental frequency for males, and a slight decrease in fundamental frequency for females. To demonstrate the efficacy of our corpus, we also conducted speech recognition experiments using two different acoustic models (DNN-HMM and transformer-based), trained with a combination of data from our corpus and speech data from three conventional Japanese speech corpora. When using the DNN-HMM trained with EARS and speech data from existing corpora, the character error rate (CER) was reduced by 7.8% (to just over 9%), compared to a CER of 16.9% when using only the baseline training corpora. We also investigated the effect of training the models with various amounts of EARS data, using a simple data expansion method. The acoustic models were also trained for various numbers of epochs without any modifications. When using the Transformer-based end-to-end speech recognizer, the character error rate was reduced by 3.0% (to 11.4%) by using a doubled EARS corpus with the baseline data for training, compared to a CER of 13.4% when only data from the baseline training corpora were used

Context-aware speech synthesis: A human-inspired model for monitoring and adapting synthetic speech

The aim of this PhD thesis is to illustrate the development a computational model for speech synthesis, which mimics the behaviour of human speaker when they adapt their production to their communicative conditions. The PhD project was motivated by the observed differences between state-of-the- art synthesiser’s speech and human production. In particular, synthesiser outcome does not exhibit any adaptation to communicative context such as environmental disturbances, listener’s needs, or speech content meanings, as the human speech does. No evaluation is performed by standard synthesisers to check whether their production is suitable for the communication requirements. Inspired by Lindblom's Hyper and Hypo articulation theory (H&H) theory of speech production, the computational model of Hyper and Hypo articulation theory (C2H) is proposed. This novel computational model for automatic speech production is designed to monitor its outcome and to be able to control the effort involved in the synthetic speech generation. Speech transformations are based on the hypothesis that low-effort attractors for a human speech production system can be identified. Such acoustic configurations are close to minimum possible effort that a speaker can make in speech production. The interpolation/extrapolation along the key dimension of hypo/hyper-articulation can be motivated by energetic considerations of phonetic contrast. The complete reactive speech synthesis is enabled by adding a negative perception feedback loop to the speech production chain in order to constantly assess the communicative effectiveness of the proposed adaptation. The distance to the original communicative intents is the control signal that drives the speech transformations. A hidden Markov model (HMM)-based speech synthesiser along with the continuous adaptation of its statistical models is used to implement the C2H model. A standard version of the synthesis software does not allow for transformations of speech during the parameter generation. Therefore, the generation algorithm of one the most well-known speech synthesis frameworks, HMM/DNN-based speech synthesis framework (HTS), is modified. The short-time implementation of speech intelligibility index (SII), named extended speech intelligibility index (eSII), is also chosen as the main perception measure in the feedback loop to control the transformation. The effectiveness of the proposed model is tested by performing acoustic analysis, objective, and subjective evaluations. A key assessment is to measure the control of the speech clarity in noisy condition, and the similarities between the emerging modifications and human behaviour. Two objective scoring methods are used to assess the speech intelligibility of the implemented system: the speech intelligibility index (SII) and the index based upon the Dau measure (Dau). Results indicate that the intelligibility of C2H-generated speech can be continuously controlled. The effectiveness of reactive speech synthesis and of the phonetic contrast motivated transforms is confirmed by the acoustic and objective results. More precisely, in the maximum-strength hyper-articulation transformations, the improvement with respect to non-adapted speech is above 10% for all intelligibility indices and tested noise conditions

Analysis on Using Synthesized Singing Techniques in Assistive Interfaces for Visually Impaired to Study Music

Tactile and auditory senses are the basic types of methods that visually impaired people sense the world. Their interaction with assistive technologies also focuses mainly on tactile and auditory interfaces. This research paper discuss about the validity of using most appropriate singing synthesizing techniques as a mediator in assistive technologies specifically built to address their music learning needs engaged with music scores and lyrics. Music scores with notations and lyrics are considered as the main mediators in musical communication channel which lies between a composer and a performer. Visually impaired music lovers have less opportunity to access this main mediator since most of them are in visual format. If we consider a music score, the vocal performer’s melody is married to all the pleasant sound producible in the form of singing. Singing best fits for a format in temporal domain compared to a tactile format in spatial domain. Therefore, conversion of existing visual format to a singing output will be the most appropriate nonlossy transition as proved by the initial research on adaptive music score trainer for visually impaired [1]. In order to extend the paths of this initial research, this study seek on existing singing synthesizing techniques and researches on auditory interfaces

Phonetic accommodation to natural and synthetic voices : Behavior of groups and individuals in speech shadowing

The present study investigates whether native speakers of German phonetically accommodate to natural and synthetic voices in a shadowing experiment. We aim to determine whether this phenomenon, which is frequently found in HHI, also occurs in HCI involving synthetic speech. The examined features pertain to different phonetic domains: allophonic variation, schwa epenthesis, realization of pitch accents, word-based temporal structure and distribution of spectral energy. On the individual level, we found that the participants converged to varying subsets of the examined features, while they maintained their baseline behavior in other cases or, in rare instances, even diverged from the model voices. This shows that accommodation with respect to one particular feature may not predict the behavior with respect to another feature. On the group level, the participants of the natural condition converged to all features under examination, however very subtly so for schwa epenthesis. The synthetic voices, while partly reducing the strength of effects found for the natural voices, triggered accommodating behavior as well. The predominant pattern for all voice types was convergence during the interaction followed by divergence after the interaction

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Audio-Visual Speech Recognition using Red Exclusion an Neural Networks

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