Assessing Performance of Bengali Speech Recognizers Under Real World Conditions using GMM-HMM and DNN based Methods

Khan, Soma; Pal, Madhab; Basu, Joyanta; Bepari, Milton Samirakshma; Roy, Rajib

doi:10.21437/sltu.2018-40

Cited by 5 publications

(2 citation statements)

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“…Impact of CS and other kinds of language switches on the ASR systems has recently received research interest, resulting in multiple approaches for CS acoustic and language modeling [7][8][9][10][11][12][13][14][15][16]. The main focus of the FAME!…”

Section: Introductionmentioning

confidence: 99%

Multi-Graph Decoding for Code-Switching ASR

et al. 2019

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In the FAME! Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's bilingual archives with CS speech. This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions. In this work, we propose a multi-graph decoding and rescoring strategy using bilingual and monolingual graphs together with a unified acoustic model for CS ASR. The proposed decoding scheme gives the freedom to design and employ alternative search spaces for each (monolingual or bilingual) recognition task and enables the effective use of monolingual resources of the high-resourced mixed language in low-resourced CS scenarios. In our scenario, Dutch is the high-resourced and Frisian is the low-resourced language. We therefore use additional monolingual Dutch text resources to improve the Dutch language model (LM) and compare the performance of single-and multi-graph CS ASR systems on Dutch segments using larger Dutch LMs. The ASR results show that the proposed approach outperforms baseline single-graph CS ASR systems, providing better performance on the monolingual Dutch segments without any accuracy loss on monolingual Frisian and code-mixed segments.

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Section: Introductionmentioning

confidence: 99%

Multi-Graph Decoding for Code-Switching ASR

et al. 2019

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“…(ii) The next step is to learn an acoustic model, usually referred to as an encoder to associate these features to higher level speech or orthographic units (phonemes or graphemes). This is achieved either using Gaussian Mixture Models (GMM) (Khan et al, 2018) or a Deep Neural Network (DNN) (Hannun et al, 2014;Amodei et al, 2016;Li et al, 2019) with HMMs, where the GMM/DNN acts as a function approximator and the HMM adds context capabilities to the encoder. With advances in deep learning, Long Short Term Memory networks (LSTMs) have replaced both the GMM/DNN and HMM models, since LSTMs act as function approximators and also learn context-dependence.…”

Section: Asr Training and Resourcesmentioning

confidence: 99%

A Survey of Multilingual Models for Automatic Speech Recognition

Yadav¹,

Sitaram²

2022

Preprint

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Although Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, the majority of the world's languages do not have usable systems due to the lack of large speech datasets to train these models. Cross-lingual transfer is an attractive solution to this problem, because low-resource languages can potentially benefit from higher-resource languages either through transfer learning, or being jointly trained in the same multilingual model. The problem of cross-lingual transfer has been well studied in ASR, however, recent advances in Self Supervised Learning are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages. In this paper, we survey the state of the art in multilingual ASR models that are built with cross-lingual transfer in mind. We present best practices for building multilingual models from research across diverse languages and techniques, discuss open questions and provide recommendations for future work.

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