Compensatory Effectiveness of Speech Recognition on the Written Composition Performance of Postsecondary Students with Learning Disabilities

Seventeen males and twelve females wrote essays under three conditions: (a) without assistance; (b) using a human transcriber; and (c) using a speech recognition system. Students received higher holistic scores using speech recognition than when writing without assistance at a statistically significant level ( p=.048). In order to determine the reasons for the superior scores on the essays written using speech recognition, 22 measures of fluency, vocabulary and syntax were computed. Several measures showed a strong correlation with the holistic score. A multiple regression revealed that the best predictor of the holistic score was Words with Seven or More Letters. Further, the ratio of Words with Seven or More Letters to Total Words differed significantly across conditions ( p=.0136), in favor of speech recognition, when compared with receiving no assistance. A factor analysis identified three factors that accounted for significant variation in holistic score: Factor 1, measures related to length of the essay ( p=.0001+); Factor 2, measures of morphological complexity ( p=.003); and Factor 3, main verbs ( p=.021). The authors suggest that the technology may have been successful because it “encouraged” the use of longer words, a powerful predictor of a holistic score.

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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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