New Features for Language Recognition From Speech Signal

Sadanandam, M.; Kamaskhiprasad, V.

doi:10.1504/ijaip.2019.10023086

Cited by 1 publication

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“…As in the state-of-the-art systems, there are popular methods to extract the feature vectors from the raw speech signal by using framing and windowed speech like Mel Frequency Cepstral Coefficients (MFCC), Prosodic, TEO, SDC features, etc. [15,16]. These feature extract methods must be similar in training and testing phases.…”

Section: Proposed Methodologymentioning

confidence: 99%

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Shivaprasad¹,

Sadanandam²

2021

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Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

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Section: Proposed Methodologymentioning

confidence: 99%

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Shivaprasad¹,

Sadanandam²

2021

View full text Add to dashboard Cite

show abstract

New Features for Language Recognition From Speech Signal

Cited by 1 publication

References 0 publications

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

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