Speech recognition of spoken Italian based on detection of landmarks and other acoustic cues to distinctive features

2020 ◽  
Vol 148 (4) ◽  
pp. 2808-2808
Author(s):  
Maria-Gabriella Di Benedetto ◽  
Jeung-Yoon Choi ◽  
Stefanie Shattuck-Hufnagel ◽  
Luca De Nardis ◽  
Sara Budoni ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Acoustic Cues ◽  
Distinctive Features

From Acoustic Cues to Distinctive Features

Phonetica ◽  
1968 ◽  
Vol 18 (4) ◽  
pp. 198-230 ◽  
Author(s):  
P. Delattre
Keyword(s):  
Acoustic Cues ◽  
Distinctive Features

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

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.

Modeling acoustic cues to distinctive features in a lexical speech analysis system

2019 ◽  
Vol 146 (4) ◽  
pp. 2960-2960
Author(s):  
Hoang Nguyen ◽  
Jeung-Yoon Choi ◽  
Stefanie Shattuck-Hufnagel
Keyword(s):  
Speech Analysis ◽  
Acoustic Cues ◽  
Distinctive Features ◽  
Analysis System

Labeling databases with individual acoustic cues to distinctive features

2020 ◽  
Vol 148 (4) ◽  
pp. 2808-2808
Author(s):  
Jeung-Yoon Choi ◽  
Stefanie Shattuck-Hufnagel
Keyword(s):  
Acoustic Cues ◽  
Distinctive Features

An L1 grammar-driven model of loanword adaptation

2014 ◽  
Vol 16 (2) ◽  
pp. 144-187 ◽  
Author(s):  
Hyunsoon Kim
Keyword(s):  
Data Collection ◽  
Distinctive Feature ◽  
Initial Position ◽  
Syllable Structure ◽  
Acoustic Cues ◽  
Distinctive Features ◽  
Loanword Adaptation ◽  
Lexical Representations ◽  
Acoustic Output ◽  
Adaptation Model

The present study proposes an L1 grammar-driven loanword-adaptation model with three intermediate steps — L1 perception, L1 lexical representations and L1 phonology — between L2 acoustic output and L1 output by examining how the distinctive features, syllable structure constraints and structural restrictions of one’s native language steer speakers in their search to replace foreign sounds with native sounds. Our main source of data in support of this model comes from differences between the Korean adaptations of English and French voicing contrasts on the basis of a recent survey of English and French loans in the year 2011. In word-initial position, for example, English voiceless plosives are borrowed as aspirated plosives, while French voiceless plosives are borrowed as either aspirated or fortis plosives in free variation. Considering the data examined here, we suggest that the different Korean adaptations of English and French voicing contrasts in plosives are based on Korean speakers’ perception of redundant phonetic variants in the donor languages (L2) and that this perception is conditioned by the acoustic cues to the laryngeal features [±spread glottis] and [±tense] of Korean, the host language (L1). In contrast to some current models, it shows that the distinctive feature composition of L1 segments plays an important role in loanword adaptations. We also suggest that not only L1 laryngeal features but also L1 syllable structure constraints and lexical restrictions influence L1 perception of the L2 voicing contrasts in word-final postvocalic plosives and that variation in vowel insertion after the plosives in our 2011 data collection is motivated by L1 phonology in both English and French loans. Variation in vowel insertion after English and French word-internal preconsonantal coda plosives is also affected by the native phonology in the 2011 data, no matter whether the plosives are released, as in French, or unreleased, as in English.

Multilingual speech annotation of landmarks and other acoustic cues to distinctive features

2021 ◽  
Vol 150 (4) ◽  
pp. A355-A355
Author(s):  
Antony Hernandez Mendoza ◽  
Jeung-Yoon Choi ◽  
Stefanie Shattuck-Hufnagel
Keyword(s):  
Acoustic Cues ◽  
Distinctive Features
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