Prosodic Features and Formant Contribution for Speech Recognition System over Mobile Network

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

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Database Creation and Dialect-Wise Comparative Analysis of Prosodic Features for Punjabi Language

Journal of Intelligent Systems ◽

10.1515/jisys-2019-2511 ◽

2019 ◽

Vol 29 (1) ◽

pp. 1275-1282

Author(s):

Shipra J. Arora ◽

Rishipal Singh

Keyword(s):

Comparative Analysis ◽

Speech Recognition ◽

Significant Variation ◽

Recognition System ◽

Speech Recognition System ◽

Prosodic Features ◽

Speech Corpus ◽

Male And Female ◽

Distinctive Features ◽

Language Analysis

Abstract The paper represents a Punjabi corpus in the agriculture domain. There are various dialects in the Punjabi language and the main concentration is on major dialects, i.e. Majhi, Malwai and Doabi for the present study. A speech corpus of 125 isolated words is taken into consideration. These words are uttered by 100 speakers, i.e. 60 Malwi dialect speakers (30 male and 30 female), 20 Majhi dialect speakers (10 male and 10 female) and 20 Doabi dialect speakers (10 male and 10 female). Tonemes, adhak (geminated) and nasal words are selected from the corpus. Recordings have been processed through two mediums. The paper also elaborates some distinctive features of the corpus. This corpus is of quite significance for the speech recognition system. Prosodic characteristics such as intonation, rhythm and stress create a crucial impact on the speech recognition system. These characteristics vary from language to language as well as various dialects of a language. This paper portrays a comparative analysis of isolated words prosodic features of Malwi, Majhi and Doabi dialects of Punjabi language. Analysis is done using the PRAAT tool. Pitch, intensity, formant I and formant II values are extracted for toneme, adhak, nasal (bindi) and nasal (tippi) words. For all kinds of words, there is a significant variation in pitch (fundamental frequency), intensity, formant I and formant II values of male and female speakers of Malwi, Majhi and Doabi dialects. A detailed analysis has been discussed throughout this paper.

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Development of HMM/Neural Network‐Based Medium‐Vocabulary Isolated‐Word Lithuanian Speech Recognition System

Informatica ◽

10.15388/informatica.2004.073 ◽

2004 ◽

Vol 15 (4) ◽

pp. 465-474 ◽

Cited By ~ 1

Author(s):

Mark Filipovič ◽

Antanas Lipeika

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Recognition System ◽

Speech Recognition System ◽

Isolated Word

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Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan ◽

10.34010/telekontran.v8i1.3078 ◽

2020 ◽

Vol 8 (1) ◽

pp. 57-73

Author(s):

Lery Sakti Ramba

Keyword(s):

Deep Learning ◽

Speech Recognition ◽

Background Noise ◽

Electronic Devices ◽

Recognition System ◽

Background Intensity ◽

Automation System ◽

Home Automation ◽

Speech Recognition System ◽

Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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