Gender independent Bangla automatic speech recognition

Hassan, Foyzul; Kotwal, Mohammed Rokibul Alam; Khan, Mohammad Saiful Alam; Huda, Mohammad Nurul

doi:10.1109/iciev.2012.6317500

Cited by 17 publications

(4 citation statements)

References 4 publications

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“…A silence /sil/ and short pause /sp/ monophone is added to create a silence model. An additional monophone /ax/ is added to indicate a short 'AA', where /aa/ indicates long 'AA' [14]. Table 2 lists some Bangla words with their written forms and the corresponding IPA.…”

Section: Development Of Bangla Asrmentioning

confidence: 99%

Performance Analysis of Different Acoustic Features Based on LSTM for Bangla Speech Recognition

Mahmud

2020

SSRN Journal

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In this work a new Bangla speech corpus along with proper transcriptions has been developed; also various acoustic feature extraction methods have been investigated using Long Short-Term Memory (LSTM) neural network to find their effective integration into a state-of-the-art Bangla speech recognition system. The acoustic features are usually a sequence of representative vectors that are extracted from speech signals and the classes are either words or sub word units such as phonemes. The most commonly used feature extraction method, known as linear predictive coding (LPC), has been used first in this work. Then the other two popular methods, namely, the Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been applied. These methods are based on the models of the human auditory system. A detailed review of the implementation of these methods have been described first. Then the steps of the implementation have been elaborated for the development of an automatic speech recognition system (ASR) for Bangla speech.

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Section: Development Of Bangla Asrmentioning

confidence: 99%

Performance Analysis of Different Acoustic Features Based on LSTM for Bangla Speech Recognition

Mahmud

2020

SSRN Journal

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“…Then 39-dimensional features are extracted and used to train triphone based HMM technique. The system was able to achieve an accuracy of 87.30% [2]. The speech recognition system is developed for Bangla accent.…”

Section: Introductionmentioning

confidence: 99%

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Mahadevaswamy

Ravì

2021

Preprint

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An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 75% of collected speech data is used for training the acoustic models and 25% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, the proposed features shows an improvement in WRA of 1.79% over baseline features.

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“…BANGLA PHONEME SCHEMES,TRIPHONE DESIGN AND BANGLA SPEECH CORPUS Bangla phonetic scheme and IPA (International Phonetic Alphabet) for Bangla were described in [16]. The paper [16] also showed characteristics of some Bangla words by using the spectrogra and triphone model based on HMM were also analyzed for Bangla words At present, a real problem to do experiment on Bangla phoneme ASR is the lack of proper Bangla speech corpus. In fact, such a corpus is not available or at least not referenced in any of the existing literature.…”

Section: Introductionmentioning

confidence: 99%

“…Again, Bangla digit recognition was found in [15]. Before us, there was no Bangla ASR system that incorporates gender specific characteristics, but our proposed method was based on Standard mel frequency cepstral coefficients (MFCCs) and consequently, it suffers from lower performance in the recognition stage [16].…”

Section: Introductionmentioning

confidence: 99%

Local Feature based Gender Independent Bangla ASR

Ahamed¹,

Mahmud²,

Rahman³

et al. 2012

IJARAI

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Abstract-This paper presents an automatic speech recognition (ASR) for Bangla (widely used as Bengali) by suppressing the speaker gender types based on local features extracted from an input speech. Speaker-specific characteristics play an important role on the performance of Bangla automatic speech recognition (ASR). Gender factor shows adverse effect in the classifier while recognizing a speech by an opposite gender, such as, training a classifier by male but testing is done by female or vice-versa. To obtain a robust ASR system in practice it is necessary to invent a system that incorporates gender independent effect for particular gender. In this paper, we have proposed a Gender-Independent technique for ASR that focused on a gender factor. The proposed method trains the classifier with the both types of gender, male and female, and evaluates the classifier for the male and female. For the experiments, we have designed a medium size Bangla (widely known as Bengali) speech corpus for both the male and female.The proposed system has showed a significant improvement of word correct rates, word accuracies and sentence correct rates in comparison with the method that suffers from gender effects using. Moreover, it provides the highest level recognition performance by taking a fewer mixture component in hidden Markov model (HMMs).

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Gender independent Bangla automatic speech recognition

Cited by 17 publications

References 4 publications

Performance Analysis of Different Acoustic Features Based on LSTM for Bangla Speech Recognition

Performance Analysis of Different Acoustic Features Based on LSTM for Bangla Speech Recognition

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Local Feature based Gender Independent Bangla ASR

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