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Automatic Speech Recognition System for Tonal Languages: State-of-the-Art Survey

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Abstract

Natural language and human–machine interaction is a very much traversed as well as challenging research domain. However, the main objective is of getting the system that can communicate in well-organized manner with the human, regardless of operational environment. In this paper a systematic survey on Automatic Speech Recognition (ASR) for tonal languages spoken around the globe is carried out. The tonal languages of Asian, Indo-European and African continents are reviewed but the tonal languages of American and Austral-Asian are not reviewed. The most important part of this paper is to present the work done in the previous years on the ASR of Asian continent tonal languages like Chinese, Thai, Vietnamese, Mandarin, Mizo, Bodo and Indo-European continent tonal languages like Punjabi, Lithuanian, Swedish, Croatian and African continent tonal languages like Yoruba and Hausa. Finally, the synthesis analysis is explored based on the findings. Many issues and challenges related with tonal languages are discussed. It is observed that the lot of work have been done for the Asian continent tonal languages i.e. Chinese, Thai, Vietnamese, Mandarin but little work been reported for the Mizo, Bodo, Indo-European tonal languages like Punjabi, Latvian, Lithuanian as well for the African continental tonal languages i.e. Hausa and Yourba.

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Author information

Authors and Affiliations

  1. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Jaspreet Kaur & Amitoj Singh

  2. Department of Informatics, School of Computer Science, University of Petroleum & Energy Studies (UPES), Energy Acres, Bidholi, Dehradun, 248007, Uttarakhand, India

    Virender Kadyan

Authors
  1. Jaspreet Kaur
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  2. Amitoj Singh
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  3. Virender Kadyan
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Correspondence to Amitoj Singh.

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Appendices

Appendix 1: A Quality Assessment Forms

1.1 Screening Question

figure a

Appendix 2: Data items Extracted from All Papers

Data item

Description

Token Initialization

Unique Identifier for the study

Data source

Author, title year

Category of article

Article from journal, conference, workshop.

Study objectives

What are the objectives of the study, i.e., search focus, i.e., the research areas of the research on which the paper focuses.

Design of study

Category of study—feature extraction, classification, SR, comparative analysis, etc.

What is the SR technique of tonal languages?

Spoken words are transcribed by the computers into readable text.

Method of comparison

Important parameters values for SR, i.e., f0, tone, energy, gain, amplitude, power, frequency response, voiced and unvoiced feature

Data analysis phase

Data analysis with respect to presentation of source, error rate, recognition accuracy

Tool and the usage

It refers to the SR tool, developer and usage of the tool

Result of study

Result or conclusions from the primary study

Appendix 3

A:

Tonal Assimilation

ACC:

Letter Accuracy

AI:

Artificial Intelligence

AMDF:

Average Magnitude Difference Function

ANN:

Artificial Neural Network

ASR:

Automatic Speech Recognition

ATWV:

Actual Term Weighted Value

BC:

Broadcast Conversational

BCC:

Broadcasting Corporation of Chinese

BLSTM:

Bidirectional Long Short Term Memory

BN:

Broadcast News

BTEC:

Basic Travel Expression Corpus

CAP:

Computer Assisted Pronunciation

CART:

Classification And Regression Tree

CD:

Context Dependent

CD-T-175:

Context Dependent Model

CE:

Consonantal Effects

CER:

Character Error Rate

CI:

Context Independent

CI-T-5:

Context Independent Model

CNN:

Convolutional Neural Network

COR:

Letter Correctness

CPM:

Compound Pseudo-Morpheme

CRF:

Conditional Random Field

CTC:

Connectionist Temporal Classification

CTS:

Conversational Telephone Speech

CV:

Consonant Vowel

D:

Declination Normalization

DBNFs:

Deep Bottle Neck Features

DCTC:

Discrete Cosine Transform Coefficient

DE:

Differential Evolution

DNN:

Deep Neural Network

DR:

Detection Rate

DRT:

Diagnostic Rhyme Test

DTW:

Dynamic Time Wrap

EMG:

Electromyography

ENV:

Environment

F:

Average Pitch Value of Whole Utterance

f0:

Fundamental Frequency

F0:

Time Periods of the Successive Human Vocal Cords Vibrations

FFT:

Fast Fourier Transform

fi:

Average Pitch Values of Voiced Segment

FLP:

Full Language Pack

G2P:

Grapheme-to-Phoneme

GA:

Genetic Algorithm

GFCC:

Gammatone Frequency Cepstral Coefficient

GMBM:

Gaussian Mixture Bigram Model

GMM:

Gaussian Mixture Model

GP:

Global Phone

HCRF:

Hidden Conditional Random Field

HINT:

Hearing In Noise Test

HMM:

Hidden Markov Model

H-T-30:

Half-Tone Model

I2R:

Institute for Infocomm Research

IC:

InterCorp

IF:

Initial/Final

ISDPs:

Intra Syllable Dependent Phone Set

KLT:

Karhunen–Loeve Transformation

KNN:

Nearest Neighbors

KWS:

Keyword Search

KWS:

Keyword Spotting

LCR:

Letter Correct Rate

LDA:

Linear Discriminant Analysis

LM:

Language Model

LM_DP_MI:

Language Model with Dynamic Programming Word Segmentation Method

LM_MM:

Language Model with Polysyllabic Words with Maximum Matching

LM1:

Closed Type Model

LM2:

Mix Type Model

LM3:

Open Type Model

LOTUS:

Large Vocabulary Thai Continuous Speech

LPCC:

Linear Predictive Cepstral Coefficient

LPM:

Latent Prosody Model

LR:

Logistic Regression

LRN 0.1:

Lithuanian Radio News Prototype Version 0.1

LRN1:

Lithuanian Radio News Version 1

LSTM:

Long Short-Term Memory

LSTM-OP:

Long Short-Term Memory with Output Projection

LSTMP:

Long Short-Term Memory Projected

LVCSR:

Large Vocabulary Continuous Speech Recognition

LVQ:

Learning Vector Quantization

MBN:

Mandarin Broadcast News

MCE:

Minimum Classification Error

MFCC:

Mel-Frequency Cepstral Coefficient

MFPLP:

Mel Frequency with Perceptual Linear Prediction

MGCPM:

Mixed Gaussian Continuous Probability Model

ML:

Maximum Likelihood

MLE:

Maximum Likelihood Estimation

MLLR:

Maximum Likelihood Linear Adaption

MLLT:

Maximum Likelihood Linear Transformation

MLP:

Multi-Layer Perceptron

MMI:

Maximum Mutual Information

MSD:

MultiSpace Distribution

MT:

Machine Translation

NCC:

Normalized Cross-Correlation

NECTEC:

National Electronics and Computer Technology Center

NECTEC-ATR:

National Electronics and Computer Technology Center Advanced Telecommunications Research

NN:

Neural Network

Norm_Log_F0_Mean_Dev:

f0 Logarithmic Value and normalizing the value of f0 by mean and standard deviation of every sentence

OOV:

Out of Vocabulary

ORCHID:

Open Linguistic Resources Channeled Toward Interdisciplinary Research

PB:

Phonetically Balanced

PCR:

Phoneme Correct Rate

PD:

Phonetically Distributed

PER:

Phone Error Rate

PLP:

Perceptual Linear Prediction

PLP:

Perceptual Linear Predictive

PM:

Pseudo-Morpheme

PNCC:

Power Normalized Cepstral Coefficients

QDA:

Quadratic Discriminant Analysis

RLAT:

Rapid Language Adaptation Toolkit

RNN:

Recurrent Neural Network

SA:

Syllable Accuracy

SBN:

Stacked Bottle Neck

SD:

Spoken Documents

SDPBMM:

State-Dependent Phoneme Based Model Merging

SER:

Sentence Error Rate

SFNNAM:

State Feedback Neural Network Activation Model

SGMM:

Subspace Gaussian Mixture Model

SLER:

Syllable Error Rate

SLM:

Syllable Lattice Matching

sMBR:

State-Level Minimum Bayes Risk

SQ:

Speech Queries

SR:

Speech Recognition

SRU:

Speech Recognition Unit

STT:

Speech-To-Text

SVM:

Support Vector Machine

TAR:

Tone Accuracy Rate

TCR:

Tone Correct Rate

TD:

Text Documents

TDRT-I:

Thai Diagnostic Rhyme Test for Initials

TF:

Tone Features

Tone 2:

High-Rising

Tone 3:

Dipping

Tone 4:

Falling

Tone 5:

Undefined

Tone1:

High-Level

TQ:

Text Queries

VERA:

Voice Encrypted Recognition Authentication

VLLP:

Very Limited Language Pack

VNBN:

Vietnamese Broadcast News

VOV:

Voice of Vietnamese

VRS:

Voice Recognition System

VRT:

Voice Recognition Technology

VSM:

Vector Space Model

WA:

Word Accuracy

WDC:

Wu Dialect Chinese

WER:

Word Error Rate

WFST:

Weighted Finite State Transducer

WU:

Word Unit

XIF:

Extended Initial/Final

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Kaur, J., Singh, A. & Kadyan, V. Automatic Speech Recognition System for Tonal Languages: State-of-the-Art Survey. Arch Computat Methods Eng 28, 1039–1068 (2021). https://doi.org/10.1007/s11831-020-09414-4

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