Malayalam Speech Recognition system and its application for visually impaired people

Anand, Anu V; Devi, P. Shobana; Stephen, Jose; Bhadran, V K

doi:10.1109/indcon.2012.6420692

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…DBN outperformed in recognizing one class while HMM is achieving higher recognition rate for the other classes. S. Salim et al [9] presented a two-stage system for spotting the boundaries of vowels, nasals, and approximants in Malayalam [10] speech signal. In the first stage, ANN is used to classify a speech signal that is classified into six broad phoneme classes.…”

Section: Related Workmentioning

confidence: 99%

Broad Phonetic Classification of ASR using Visual Based Features

Lehabik¹,

Merzban

Saad³

et al. 2020

The Egyptian Journal of Language Engineering

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This paper presents a novel method of classifying speech phonemes. Four hybrid techniques based on the acousticphonetic approach and pattern recognition approach are used to emphasize the principle idea of this research. The first hybrid model is constructed of fixed state, structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network, Vector Quantization (FS-HMM-GM-MBTI-CNN-VQ). The second hybrid model is constructed of variable state, dynamically structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network, Vector Quantization (VS-HMM-GM-MBTI-CNN-VQ). The third hybrid model is constructed of fixed state, structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network (FS-HMM-GM-MBTI-CNN). The fourth hybrid model is constructed of variable state, dynamically structured Hidden Markov Model, Gaussian Mixture, Mel scaled Best Tree Image, Convolution Neural network (VS-HMM-GM-MBTI-CNN). TIMIT database is used in this paper. All phones are classified into five classes and segregated into Vowels, Plosives, Fricatives, Nasals, and Silences. The results show that using (VS-HMM-GM-MBTI-CNN-VQ) is an available method for classification of phonemes, with the potential for use in applications such as automatic speech recognition and automatic language identification. Competitive results are achieved especially in nasals, plosives, and silence high successive rates than others.

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Section: Related Workmentioning

confidence: 99%

Broad Phonetic Classification of ASR using Visual Based Features

Lehabik¹,

Merzban

Saad³

et al. 2020

The Egyptian Journal of Language Engineering

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“…Anand in [5] developed a modern ASR of wide vocabulary with an application in people with visual disabilities. In feature extraction phase, MFCC was used and in the classification and recognition phase an acoustic model was developed using thirty hours of HMM-based audio.…”

Section: Related Workmentioning

confidence: 99%

“…Consider two time series P and Q with a length of n and m respectively. P = p1, p2, p3, .... pn Q = q1, q2, q3, .... qm An mxn matrix is built and for each intersection the distance between the two points (pi, qj) is calculated using the Euclidean Distance formula described in (5).…”

Section: Classification and Recognitionmentioning

confidence: 99%

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Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Chuctaya¹,

Mercado²,

Gaona³

2018

ijacsa

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The Automatic Speech (ASR) area is defined as the transformation of acoustic signals into string words. This area has been being developed for many year facilitating the lives of people so it was implemented in several languages. However, the development of ASR in some languages with few database resources but with a large population speaking these languages is very low. The development of ASR in Quechua language is almost null which leads culture and population isolation from technology and information. In this work an ASR system of isolated Quechua numbers is developed where Mel-Frequency Cepstral Coefficients (MFCC), Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) methods are implemented using a database composed by recorded audio numbers from one to ten in Quechua. The recorded audios to feed the data base were uttered by natives man and women speakers of Quechua. The recognition accuracy reached in this research work was 91.1%.

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“…There are several previous attempts for building ASR in Malayalam, but it deals with the speech in constrained domain [3]- [5]. Malayalam variations or regional variations were not tackled in the earlier works.…”

Section: Introductionmentioning

confidence: 99%

Prosodically guided phonetic engine

Deekshitha

Mary

2015

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

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Phonetic Engine (PE) is the first stage of automatic speech recognition system that converts input speech to a sequence of phonetic symbols. A baseline phonetic engine is created using Malayalam speech database. A Graphical User Interface (GUI) is developed for the phonetic engine to perform real time recognition of phonemes. It is known that higher level of speech information such as intonation, duration and intensity collectively referred as 'prosody', aids human speech recognition. Prosody helps to segment speech to sentences/phrases and to disambiguate recognition process. This has motivated us to incorporate prosody for the improvement of the baseline phonetic engine. However incorporating prosody in automatic speech recognition is a challenging task. This paper describes an approach to automatic labeling of prosodic events and discusses about the possibility to implement a prosodically guided phonetic engine for Malayalam. Automatic phrase-like segmentation is realized by detecting long pauses with an Artificial Neural Network (ANN) based classifier. Broad Phoneme Classification is achieved using features derived from the speech at the signal level itself. Combination of broad phoneme transcription and pitch trend labels is used to obtain a temporal prosodic pattern. We have illustrated the effectiveness of this temporal prosodic pattern is for audio search application.

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Malayalam Speech Recognition system and its application for visually impaired people

Cited by 11 publications

References 7 publications

Broad Phonetic Classification of ASR using Visual Based Features

Broad Phonetic Classification of ASR using Visual Based Features

Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN

Prosodically guided phonetic engine

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