Phonetic and Prosodically Rich Transcribed speech corpus in Indian languages: Bengali and Odia

Kumar, Sunil; Rao, K. Sreenivasa; Pati, Debadatta

doi:10.1109/icsda.2013.6709901

Cited by 21 publications

(2 citation statements)

References 6 publications

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“…In this study, we have used the "Phonetic and Prosodically Rich Transcribed" (PPRT) Bengali speech corpus developed at IIT Kharagpur [10]. PPRT contains speech data in read, extempore and conversation modes of speech.…”

Section: B Bengali Speech Corpusmentioning

confidence: 99%

Improvement of phone recognition accuracy using source and system features

Manjunath

Rao

Reddy

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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The goal of this work is to improve phone recognition accuracy using combination of source and system features. As speech is produced by exciting time varying vocal tract system with time varying excitation, we want to explore both source and system components of speech production system for phone recognition. The excitation source information is derived by processing linear prediction residual of speech signal. Melfrequency cepstral coefficient features are used for capturing vocal tract information. The Phone Recognition Systems (PRSs) are developed using hidden markov models. The proposed PRSs are developed for English and an Indian language Bengali using TIMIT and Phonetic, Prosodically Rich Transcribed speech corpora, respectively. We have also developed tandem PRSs using the phone posteriors obtained from feedforward neural networks. The tandem PRSs developed using combination of excitation source and system features, outperform the conventional tandem systems developed using system features alone.

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Section: B Bengali Speech Corpusmentioning

confidence: 99%

Improvement of phone recognition accuracy using source and system features

Manjunath

Rao

Reddy

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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“…is standard classifier for various speech applications [16,17,18] corpora are given in [19,20]. The speech signals are sampled at a rate of 16 KHz with 16 bits per sample.…”

Section: Introductionmentioning

confidence: 99%

Multilingual and multimode phone recognition system for Indian languages

Tripathi

Reddy

Rao

2020

Speech Communication

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The aim of this paper is to develop a flexible framework capable of automatically recognizing phonetic units present in a speech utterance of any language spoken in any mode. In this study, we considered two modes of speech: conversation, and read modes in four Indian languages, namely, Telugu, Kannada, Odia, and Bengali. The proposed approach consists of two stages: (1) Automatic speech mode classification (SMC) and(2) Automatic phonetic recognition using mode-specific multilingual phone recognition system (MPRS). In this work, vocal tract and excitation souce features are considered for speech mode classification (SMC) task. SMC systems are developed using multilayer perceptron (MLP). Further, vocal tract, excitation source, and tandem features are used to build the deep neural network (DNN)-based MPRSs. The performance of the proposed approach is compared with mode-dependent MPRSs. Experimental results show that the proposed approach which combines both SMC and MPRS into a single system outperforms the baseline mode-dependent MPRSs.

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Development and Analysis of Multilingual Phone Recognition System

Manjunath

2021

SpringerBriefs in Speech Technology

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Phonetic and Prosodically Rich Transcribed speech corpus in Indian languages: Bengali and Odia

Cited by 21 publications

References 6 publications

Improvement of phone recognition accuracy using source and system features

Improvement of phone recognition accuracy using source and system features

Multilingual and multimode phone recognition system for Indian languages

Development and Analysis of Multilingual Phone Recognition System

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