Automatic speech recognition for Bangla digits

Muhammad, Ghulam; Alotaibi, Yousef Ajami; Huda, Mohammad Nurul

doi:10.1109/iccit.2009.5407267

Cited by 50 publications

(14 citation statements)

References 2 publications

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“…For features extraction MFCC algorithm and for recognition HMM classifier was used. The result was more than 95 % for 0-5 digits, and for 6-9 digits the result was less than 90 % (Muhammad et al 2009). Majid developed Automatic Speech Recognition System for Malay digits.…”

Section: Introductionmentioning

confidence: 86%

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Ali

Abbas

Thasleema

et al. 2015

Int J Speech Technol

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Automatic recognition of isolated spoken digits is one of the most challenging tasks in the area of Automatic Speech Recognition. In this paper, Database Development and Automatic Speech Recognition of Isolated Pashto Spoken Digits from Sefer (0) to Naha (9) has been presented. A number of 50 individual Pashto native speakers (25 male and 25 female) of different ages, ranging from 18 to 60 years, were involved to utter from Sefer (0) to Naha (9) digits separately. Sony PCM-M 10 linear recorder is used for recoding purpose in the office and home in noise free environment. Adobe audition version 1.0 is used to split the audio of digits into individual digits and result is saved in .wav format. Mel frequency cepstral coefficients is used to extract speech features. K nearest neighbor classifier is used for the first time up to author knowledge in Pashto language to classify the features of speech and compare its accuracy with linear discriminate analysis. The experimental results are evaluated, and the overall average recognition exactitude of 76.8 % is obtained.

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Section: Introductionmentioning

confidence: 86%

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Ali

Abbas

Thasleema

et al. 2015

Int J Speech Technol

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“…The Phonetic inventory of Bangla consists of 14 vowels, including seven nasalized vowels, and 29 consonants (Muhammad et al 2009; http://en.wikipedia.org/wiki/ Bengali_phonology). An approximate phonetic scheme in IPA is given in Table 1.…”

Section: Bangla Phonemesmentioning

confidence: 99%

“…Bangla phonetic scheme in IPA ((Muhammad et al 2009); http://en.wikipedia.org/wiki/Bengali_phonology)…”

mentioning

confidence: 99%

MLN-based Bangla ASR using context sensitive triphone HMM

Hassan

Kotwal

Muhammad

et al. 2011

Int J Speech Technol

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Building a continuous speech recognizer for the Bangla (widely used as Bengali) language is a challenging task due to the unique inherent features of the language like long and short vowels and many instances of allophones. Stress and accent vary in spoken Bangla language from region to region. But in formal read Bangla speech, stress and accents are ignored. There are three approaches to continuous speech recognition (CSR) based on the sub-word unit viz. word, phoneme and syllable. Pronunciation of words and sentences are strictly governed by set of linguistic rules. Many attempts have been made to build continuous speech recognizers for Bangla for small and restricted tasks. However, medium and large vocabulary CSR for Bangla is relatively new and not explored. In this paper, the authors have attempted for building automatic speech recognition (ASR) method based on context sensitive triphone acoustic models. The method comprises three stages, where the first stage extracts phoneme probabilities from acoustic features using a multilayer neural network (MLN), the second stage designs triphone models to catch context of both sides and the final stage generates word strings based on triphone hidden Markov models (HMMs). The objective of this research is to build a medium vocabulary triphone based continuous speech recognizer for Bangla language. In this experimentation using Bangla speech corpus prepared by us, the recognizer provides higher word accuracy as well as word correct rate for trained and tested sentences with fewer mixture components in HMMs.

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“…Again, Bangla digit recognition was found in [15]. Since no work in Bangla was found for suppressing the gender factor, previously, we proposed a method [16] for that purpose by embedding multiple HMM-based classifiers.…”

Section: Introductionmentioning

confidence: 99%

Gender Effect Canonicalization for Bangla ASR

Rahman¹,

Ahamed²,

Asfak-Ur-Rahman³

et al. 2012

IJACSA

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Abstract-This paper presents a Bangla (widely used as Bengali) automatic speech recognition system (ASR) by suppressing gender effects. Gender characteristic plays an important role on the performance of ASR. If there is a suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In the proposed method, we have designed a new ASR incorporating the Local Features (LFs) instead of standard mel frequency cepstral coefficients (MFCCs) as an acoustic feature for Bangla by suppressing the gender effects, which embeds three HMM-based classifiers for corresponding male, female and geneder-independent (GI) characteristics.In the experiments on Bangla speech database prepared by us, the proposed system has achieved a significant improvement of word correct rates (WCRs), word accuracies (WAs) and sentence correct rates (SCRs) in comparison with the method that incorporates Standard MFCCs.

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Automatic speech recognition for Bangla digits

Cited by 50 publications

References 2 publications

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

MLN-based Bangla ASR using context sensitive triphone HMM

Gender Effect Canonicalization for Bangla ASR

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