Abstract

In this paper, a new method to detect disfluencies directly from speech is explored. The method makes use of pre-linguistic automatic syllabification - the process of segmenting input speech signals into perceptually distinct syllable-like regions, to develop syllable-level disfluency detection systems. Statistical prosody features related to fundamental frequency, energy and duration are extracted from each syllable-like region and used to train a DNN classifier for automatic detection of speech disfluencies. Further, complementary information useful for the task of disfluency detection is added to the pipeline with the help of acoustic features. A BiLSTM feature extractor is used to get complex acoustic representation from the baseline MFCC features for each syllable-like region. This acoustic representation is concatenated with the prosody features and used in the proposed system for detecting multiple speech disfluencies. Experiments are conducted for four types of disfluencies in the UCLASS and the IIITH-IED datasets to test the proposed disfluency detection system. Overall, it is found that the proposed system gives a detection accuracy of 88.75% for the disfluencies in the UCLASS dataset, whereas for the IIITH-IED dataset, the accuracy obtained is 91.24%, showing the effectiveness of considering perceptually distinct syllable-like regions as representational units for detecting disfluencies.

Abstract

Objective assessment of voice disorders is widely explored as an early diagnosis tool for the classification of voice disorders. Voice disorders affect the pitch, loudness and voice quality, which are perceived at the suprasegmental-level in the speech signal. For the detection and assessment of voice disorders, this study explores the effectiveness of Long Term Average Spectral (LTAS) features using four state-of-the-art filter banks designed with critical-band, constant-Q, gammatone, and singlefrequency filtering approaches. Moreover, the performance of the systems is compared with state-of-the-art statistical-average and openSMILE features. Voice disorder detection experiment was carried out on SVD and HUPA database, while only SVD database is used for assessment task. Assessment task is performed in clinical way, in which four binary classifiers were trained in our study. Voice disorder detection and assessment tasks were carried out using the support vector machine classifier. From the results, it was observed that constant-Q filter bank based LTAS features performed better among all LTAS features with classification accuracy of 78% and 81.4% for voice disorder detection task on SVD and HUPA database, respectively. Further, the combination of LTAS features with OpenSMILE features improved (89.6% and 86.6% for SVD and HUPA database, respectively) the performance.

Abstract

Speech is a natural mode of communication among all beings. India is a densely populated country, and people are diverse throughout the globe. The spoken language is the medium of instruction to interact among the people. The majority of Indian languages are spoken globally. The unavailability of larger volumes of transcribed and annotated speech data is often a hurdle for building reliable speech recognition (ASR) systems for Indian languages. Crowdsourcing strategies are effective in collaboratively collecting speech data resources. This paper describes the experience of large-scale speech data collection for the Telugu language through mobile and web-based applications. With this crowd contributed speech, the performance of the baseline ASR system is shown for clean speech. ASR performance for pink and white noises is also compared for various deep neural network (DNN) based acoustic models. The details regarding the usage of frameworks and their challenges during their implementation are part of this paper. The framework adopted for collecting the speech data is rapid, cost-saving, and offers the advantage of extending it to all the other Indian languages. Index Terms—ASR, Crowd-sourced, TDNN, GMM, SGMM

Abstract

Speech-to-Speech Machine Translation (SSMT) applications and services use a three-step process. Speech recognition is the first step to obtain transcriptions. This is followed by text-to-text language translation and, finally, synthesis into text-speech. As data availability and computing power improved, these individual steps evolved. However, despite significant progress, there is always the error of the first stage in terms of speech recognition, accent, etc. Having traversed the speech recognition stage, the error becomes more prevalent and decreases very often. This chapter presents a complete pipeline for transferring speaker intent in SSMT involving humans in the loop. Initially, the SSMT pipeline has been discussed and analyzed for broadcast speeches and talks on a few sessions of Mann Ki Baat, where the source language is in Hindi, and the target language is in English and Telugu. To perform this task, industry-grade APIs from Google, Microsoft, CDAC, and IITM has been used for benchmarking. Later challenges faced while building the pipeline are discussed, and potential solutions have been introduced. Later this chapter introduces a framework developed to

Abstract

Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing methods explore the short-term spectral information from speech to assess hy-pernasality. The present work examines long-term average spectral (LTAS) features obtained from speech to detect and assess hyper-nasality. This work proposes single frequency filter bank based long-term average spectral (SFFB-LTAS) features for hypernasality detection and assessment. The SFFB is used to extract long-term average spectra with a good spectral resolution. The experiments are carried out using NMCPC-CLP database collected from 41 speakers with CLP and 32 speakers without CLP. The experimental results show that, SFFB-LTAS features performed better compared to state-of-art spectral and prosody features. The proposed systems for the detection and assessment of hypernasality have shown classification accuracy of 89% and 82.1%, respectively.

Abstract

The objectives of the dysarthria assessment are to discriminate dysarthric speech from normal speech, to estimate the severity of dysarthria in terms of the dysarthric speech intelligibility, and to find the motor speech subsystem which causes defects in speech production. In this work, analytic phase features are investigated for the objective assessment of dysarthria. In this connection, the importance of analytic phase in speech intelligibility is studied by employing phase modification schemes. The current investigation on the analytic phase, proposed a novel approach to estimate the instantaneous frequency components from the speech signal, by using single frequency filtering technique. In this study, dysarthric speech detection and intelligibility assessment systems are developed by using UA-Speech database. The efficiency of the analytic phase features is compared with state-of-the-art spectral features. The proposed features outperformed the magnitude and group delay features and shown a classification accuracies of 95.61% and 64.47% for dysarthric speech detection and intelligibility assessment tasks, respectively. The fusion of the evidence obtained from the analytic phase and magnitude spectral features revealed the complementary nature of analytic phase features.

Abstract

Identifying language information from speech utterance is referred to as spoken language identification. Language Identification (LID) is essential in multilingual speech systems. The performance of LID systems have been studied for various adverse conditions such as background noise, telephonic channel, short utterances, so on. In contrast to these studies, for the first time in the literature, the present work investigated the impact of emotional speech on language identification. In this work, different emotional speech databases have been pooled to create the experimental setup. Additionally, state-of-art i-vectors, timedelay neural networks, long short term memory, and deep neural network x-vector systems have been considered to build the LID systems. Performance of the LID system has been evaluated for speech utterances of different emotions in terms of equal error rate and Cavg. The results of the study indicate that the speech utterances of anger and happy emotions degrades performance of LID systems more compared to the neutral and sad emotions. Index Terms—Language Identification, i-vector, TDNN, LSTM, DNN x-vector

Abstract

Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in dysarthria diagnosis and therapy. Since speakers with dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant /ɹ/ distinguishes dysarthric speech of different severity levels. Duration measurements were conducted using the third formant (F3) trajectories estimated from quasi-closed-phase (QCP) spectrograms. Results indicate that the severity level of spastic dysarthria has a significant effect on duration of /ɹ/. In addition, the phonetic context has a significant effect on duration of /ɹ/, the ɪ-r-ɛ context showing the largest difference in /ɹ/ duration between dysarthric speech of the highest severity levels and healthy speech. The results of this preliminary study can be used in the future to develop signal processing and machine learning methods to automatically predict the severity level of spastic dysarthria from speech signals.

Abstract

Fricatives are characterized by two prime acoustic properties, i.e., having high-frequency spectral concentration and possessing noisy nature. Spectral domain approaches for detecting fricatives employ a time–frequency representation to compute acoustic cues such as band energy ratio, spectral centroid, and dominant resonant frequency. The detection accuracy of these approaches depends on the efficiency of the employed time–frequency representation. An approach that would not require any time–frequency representation for detecting fricatives from speech has been explored in this work. In this study, a time-domain operation is proposed which emphasizes the high-frequency spectral characteristics of fricatives implicitly. The proposed approach aims to scale the spectrum of the speech signal using a scaling function k2, where k is the discrete frequency. The spectral weighting function used in the proposed approach can be approximated as a cascaded temporal difference operation over speech signal. The emphasized regions in spectrally weighted speech signal are quantified to detect fricative regions. Contrasting the spectral domain approaches, the predictability measure-based approach in literature relies on capturing the noisy nature of fricatives. The proposed approach and the predictability measure-based approaches rely on two complementary properties for detecting fricatives, and a combination of these approaches is put forth in this work. The proposed approach has performed better than the state-of-the-art fricative detectors. To study the significance of the proposed evidence, an early fusion between the proposed evidence and the feature-space maximum log-likelihood transform features is explored for developing speech recognition systems.

Abstract

Epoch is an abrupt closure event within a glottal cycle at which significant excitation to the vocal-tract system happens during the production of voiced speech. The state-of-the-art zero frequency filtering technique is a simple and efficient method that shows robustness in extracting the epochs from cleanspeech.However,this methodhasshownpoor performance for telephonic quality speech, due to the presence of spurious zero crossings in epoch evidence, which leads to a high false alarm rate. Recently,zero-phase zero frequency resonator(ZP-ZFR) an alternative tozero frequency filter isproposed for stable implementation of zero frequency filtering technique. In this study,higher-order ZP-ZFR is investigated to improve the performance of zero frequency filtering for epoch extraction from telephonic speech. The performance of the proposedZP-ZFR method is quantitatively evaluated on telephonic speech simulated usingsix standard databases having simultaneous electroglottograph recordings as ground truth. Experimental results suggest that the performance of the proposed method is significantly better than the state-of-the-art methods in terms of identification rate and false alarm rate.