160 research outputs found
Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey
Speaker-independent VSR is a complex task that involves identifying spoken
words or phrases from video recordings of a speaker's facial movements. Over
the years, there has been a considerable amount of research in the field of VSR
involving different algorithms and datasets to evaluate system performance.
These efforts have resulted in significant progress in developing effective VSR
models, creating new opportunities for further research in this area. This
survey provides a detailed examination of the progression of VSR over the past
three decades, with a particular emphasis on the transition from
speaker-dependent to speaker-independent systems. We also provide a
comprehensive overview of the various datasets used in VSR research and the
preprocessing techniques employed to achieve speaker independence. The survey
covers the works published from 1990 to 2023, thoroughly analyzing each work
and comparing them on various parameters. This survey provides an in-depth
analysis of speaker-independent VSR systems evolution from 1990 to 2023. It
outlines the development of VSR systems over time and highlights the need to
develop end-to-end pipelines for speaker-independent VSR. The pictorial
representation offers a clear and concise overview of the techniques used in
speaker-independent VSR, thereby aiding in the comprehension and analysis of
the various methodologies. The survey also highlights the strengths and
limitations of each technique and provides insights into developing novel
approaches for analyzing visual speech cues. Overall, This comprehensive review
provides insights into the current state-of-the-art speaker-independent VSR and
highlights potential areas for future research
Multimodal Based Audio-Visual Speech Recognition for Hard-of-Hearing: State of the Art Techniques and Challenges
Multimodal Integration (MI) is the study of merging the knowledge acquired by the nervous system using sensory modalities such as speech, vision, touch, and gesture. The applications of MI expand over the areas of Audio-Visual Speech Recognition (AVSR), Sign Language Recognition (SLR), Emotion Recognition (ER), Bio Metrics Applications (BMA), Affect Recognition (AR), Multimedia Retrieval (MR), etc. The fusion of modalities such as hand gestures- facial, lip- hand position, etc., are mainly used sensory modalities for the development of hearing-impaired multimodal systems. This paper encapsulates an overview of multimodal systems available within literature towards hearing impaired studies. This paper also discusses some of the studies related to hearing-impaired acoustic analysis. It is observed that very less algorithms have been developed for hearing impaired AVSR as compared to normal hearing. Thus, the study of audio-visual based speech recognition systems for the hearing impaired is highly demanded for the people who are trying to communicate with natively speaking languages. This paper also highlights the state-of-the-art techniques in AVSR and the challenges faced by the researchers for the development of AVSR systems
Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition
Vowel Recognition is a part of automatic speech recognition (ASR) systems that classifies speech signals into groups of vowels. The performance of Malay vowel recognition (MVR) like any multiclass classification problem depends largely on Feature Vectors (FVs). FVs such as Mel-frequency Cepstral Coefficients (MFCC) have produced high error rates due to poor phoneme information. Classifier transformed probabilistic features have proved a better alternative in conveying phoneme information. However, the high dimensionality of the probabilistic features introduces additional complexity that deteriorates ASR performance. This study aims
to improve MVR performance by proposing an algorithm that transforms MFCC FVs into a new set of features using Multinomial Logistic Regression (MLR) to reduce the dimensionality of the probabilistic features. This study was carried out in four phases
which are pre-processing and feature extraction, best regression coefficients generation, feature transformation, and performance evaluation. The speech corpus consists of 1953 samples of five Malay vowels of /a/, /e/, /i/, /o/ and /u/ recorded from students of two public universities in Malaysia. Two sets of algorithms were developed which are DBRCs and FELT. DBRCs algorithm determines the best regression coefficients (DBRCs) to obtain the best set of regression coefficients (RCs) from the extracted 39-MFCC FVs through resampling and data swapping approach. FELT
algorithm transforms 39-MFCC FVs using logistic transformation method into FELT FVs. Vowel recognition rates of FELT and 39-MFCC FVs were compared using four different classification techniques of Artificial Neural Network, MLR, Linear Discriminant Analysis, and k-Nearest Neighbour. Classification results showed that FELT FVs surpass the performance of 39-MFCC FVs in MVR. Depending on the classifiers used, the improved performance of 1.48% - 11.70% was attained by FELT over MFCC. Furthermore, FELT significantly improved the recognition accuracy of
vowels /o/ and /u/ by 5.13% and 8.04% respectively. This study contributes two algorithms for determining the best set of RCs and generating FELT FVs from MFCC. The FELT FVs eliminate the need for dimensionality reduction with comparable performances. Furthermore, FELT FVs improved MVR for all the five vowels
especially /o/ and /u/. The improved MVR performance will spur the development of Malay speech-based systems, especially for the Malaysian community
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Pattern Matching for Translating Domain-Specific Terms from Large Corpora
Translating domain-specific terms is one significant component of machine translation and Machine-aided translation systems. These terms are often not found in standard dictionaries. Human translators, not being experts in every technical or regional domain, cannot produce their translations effectively. Automatic translation of domain-specific terms is therefore highly desirable. Most other work on automatic term translation uses statistical information of words from parallel corpora. Parallel corpora of clean- translated texts are hard to come by whereas there are more noisy- translated texts and many more monolingual texts in various domains. We propose using noisy parallel texts and same-domain texts of a pair of languages to translate terms. In our work, we propose using a novel paradigm of pattern matching of statistical signals of word features. These features are robust to the syntactic structure, character sets, language of the text, and to the domain. We obtain statistical information which is related to the lexical properties of a word and its translation in any other language of the same domain. These lexical properties are extracted from the corpora and represented in vector form. We propose using signal processing techniques for matching these features vectors of a word to those of its translation. Another matching technique we propose is applying discriminative analysis of the word features. For each word, the various features are combined into a single vector which is then transformed into a smaller dimension eigenvector for matching. Since most domain specific terms are nouns and noun phrases, we concentrate on translating English nouns and noun phrases into other languages. We study the relationship between English noun phrases and their translations in Chinese, Japanese and French in parallel corpora. The result of this study is used in our system for translation of English noun phrases into these other languages from noisy parallel and non-parallel corpora
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
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