Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed

Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker. Thus although, we have multiple camera feeds for the speech of a user, but we have failed in using these multiple video feeds for dealing with the different poses. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple camera views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.Comment: 2018 ACM Multimedia Conference (MM '18), October 22--26, 2018, Seoul, Republic of Kore

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Melody as Prosody: Toward a Usage-Based Theory of Music

MELODY AS PROSODY: TOWARD A USAGE-BASED THEORY OF MUSIC Thomas M. Pooley Gary A. Tomlinson Rationalist modes of inquiry have dominated the cognitive science of music over the past several decades. This dissertation contests many rationalist assumptions, including its core tenets of nativism, modularity, and computationism, by drawing on a wide range of evidence from psychology, neuroscience, linguistics, and cognitive music theory, as well as original data from a case study of Zulu song prosody. An alternative biocultural approach to the study of music and mind is outlined that takes account of musical diversity by attending to shared cognitive mechanisms. Grammar emerges through use, and cognitive categories are learned and constructed in particular social contexts. This usage-based theory of music shows how domain-general cognitive mechanisms for patterning-finding and intention-reading are crucial to acquisition, and how Gestalt principles are invoked in perception. Unlike generative and other rationalist approaches that focus on a series of idealizations, and the cognitive `competences\u27 codified in texts and musical scores, the usage-based approach investigates actual performances in everyday contexts by using instrumental measures of process. The study focuses on song melody because it is a property of all known musics. Melody is used for communicative purposes in both song and speech. Vocalized pitch patterning conveys a wide range of affective, propositional, and syntactic information through prosodic features that are shared by the two domains. The study of melody as prosody shows how gradient pitch features are crucial to the design and communicative functions of song melodies. The prosodic features shared by song and speech include: speech tone, intonation, and pitch-accent. A case study of ten Zulu memulo songs shows that pitch is not used in the discrete or contrastive fashion proposed by many cognitive music theorists and most (generative) phonologists. Instead there are a range of pitch categories that include pitch targets, glides, and contours. These analyses also show that song melody has a multi-dimensional pitch structure, and that it is a dynamic adaptive system that is irreducible in its complexity

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Articulatory representations to address acoustic variability in speech

The past decade has seen phenomenal improvement in the performance of Automatic Speech Recognition (ASR) systems. In spite of this vast improvement in performance, the state-of-the-art still lags significantly behind human speech recognition. Even though certain systems claim super-human performance, this performance often is sub-par across domains and across datasets. This gap is predominantly due to the lack of robustness against speech variability. Even clean speech is extremely variable due to a large number of factors such as voice characteristics, speaking style, speaking rate, accents, casualness, emotions and more. The goal of this thesis is to investigate the variability of speech from the perspective of speech production, put forth robust articulatory features to address this variability, and to incorporate these features in state-of-the-art ASR systems in the best way possible. ASR systems model speech as a sequence of distinctive phone units like beads on a string. Although phonemes are distinctive units in the cognitive domain, their physical realizations are extremely varied due to coarticulation and lenition which are commonly observed in conversational speech. The traditional approaches deal with this issue by performing di-, tri- or quin-phone based acoustic modeling but are insufficient to model longer contextual dependencies. Articulatory phonology analyzes speech as a constellation of coordinated articulatory gestures performed by the articulators in the vocal tract (lips, tongue tip, tongue body, jaw, glottis and velum). In this framework, acoustic variability is explained by the temporal overlap of gestures and their reduction in space. In order to analyze speech in terms of articulatory gestures, the gestures need to be estimated from the speech signal. The first part of the thesis focuses on a speaker independent acoustic-to-articulatory inversion system that was developed to estimate vocal tract constriction variables (TVs) from speech. The mapping from acoustics to TVs was learned from the multi-speaker X-ray Microbeam (XRMB) articulatory dataset. Constriction regions from TV trajectories were defined as articulatory gestures using articulatory kinematics. The speech inversion system combined with the TV kinematics based gesture annotation provided a system to estimate articulatory gestures from speech. The second part of this thesis deals with the analysis of the articulatory trajectories under different types of variability such as multiple speakers, speaking rate, and accents. It was observed that speaker variation degraded the performance of the speech inversion system. A Vocal Tract Length Normalization (VTLN) based speaker normalization technique was therefore developed to address the speaker variability in the acoustic and articulatory domains. The performance of speech inversion systems was analyzed on an articulatory dataset containing speaking rate variations to assess if the model was able to reliably predict the TVs in challenging coarticulatory scenarios. The performance of the speech inversion system was analyzed in cross accent and cross language scenarios through experiments on a Dutch and British English articulatory dataset. These experiments provide a quantitative measure of the robustness of the speech inversion systems to different speech variability. The final part of this thesis deals with the incorporation of articulatory features in state-of-the-art medium vocabulary ASR systems. A hybrid convolutional neural network (CNN) architecture was developed to fuse the acoustic and articulatory feature streams in an ASR system. ASR experiments were performed on the Wall Street Journal (WSJ) corpus. Several articulatory feature combinations were explored to determine the best feature combination. Cross-corpus evaluations were carried out to evaluate the WSJ trained ASR system on the TIMIT and another dataset containing speaking rate variability. Results showed that combining articulatory features with acoustic features through the hybrid CNN improved the performance of the ASR system in matched and mismatched evaluation conditions. The findings based on this dissertation indicate that articulatory representations extracted from acoustics can be used to address acoustic variability in speech observed due to speakers, accents, and speaking rates and further be used to improve the performance of Automatic Speech Recognition systems