Connected digit speech recognition system for Malayalam language

Abstract

Abstract.A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer for Malayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.

Two‐stage spoken term detection system for under‐resourced languages

“…Due to the non‐availability of standard labelled corpora, Malayalam can be considered as low resourced languages. Some efforts have been taken place to develop phone recognition systems in Malayalam using mono‐phone models such as Malayalam digit recognition [52, 53 ]. Earlier we have developed an HTK toolkit based phonetic engine [54 ] and a Sphinx ‐based recognition system [55 ] for Malayalam.…”

Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network

“…Tn any traditional speech recognition application, feature extraction is mandatory to have a well representing vector of the input raw data. For isolated spoken digit recognition, as any speech recognition application many features are proposed in the literature such as: cepstrum features [2], mel frequency cepstral coefficients (MFCCs) [4][5][6][7], perceptual linear predictive (PLP) [8] and weighted MFCC (WMFCC) [9]. While for classification purpose, there are many proposed techniques for the same application, for example, Allosh et al [1] consider tow techniques which are pitch detection algorithm (PDA) [2,6] and cepstrum correlation algorithm (CCA) [7] for digit recognition with a database of spoken Arabic digits (0-9) that consists of (3 males and 3 females) with speech recording in length of 12 seconds.…”

“…An isolated English digit (0-9) are recorded and an accuracy of 90.5% was achieved for 100 samples. Kurian and Balakrishnan [8] offer a mechanism to speaker-independent connected digit recognizer for Malayalam language using PLP cepstral coefficient for speech parameterization and continuous density HMM to the recognition process. The used training data consists of 21 subjects with ages of 20 to 40 years old.…”

N-Gram Language Model based Continuous Voiced Odia Digit Recognition

“…In [30], an isolated Kannada digit recognition model was developed by using HMM and MFCC. An optimal speaker independent continuous digit recognizer for Malayalam language was proposed by C. Kurian and K. Balakrishnan [18]. For speech parameterization, the system employs PLP coefficient technique whereas for recognition purpose HMM model is used.…”