Native and non-native class discrimination using speech rhythm- and auditory-based cues

Selouani, Sid‐Ahmed; Alotaibi, Yousef Ajami; Cichocki, Wladyslaw; Gharsellaoui, Soumaya; Kadi, Kamil Lahcene

doi:10.1016/j.csl.2014.11.003

Cited by 8 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies of L2 rhythm have focused on patterns of duration across an utterance, which motivated the selection and addition to SpeechRater of duration‐based rhythm features. However, some studies have also considered other acoustic properties that contribute to the perception of a nonnative speaker's rhythm; in particular, He () and Selouani, Alotaibi, Cichocki, Gharsellaoui, and Kadi () found systematic rhythmic differences between L1 and L2 speech based on patterns of average intensity values across linguistic intervals, similar to the duration‐based rhythm metrics percentX, stddevX, varcoX, and rpviX. Future research will address whether these additional types of features can improve SpeechRater's automated assessment of nonnative rhythm in addition to the duration‐based features.…”

Section: Discussionmentioning

confidence: 99%

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine

Chen

Zechner

Yoon

et al. 2018

ETS Research Report Series

View full text Add to dashboard Cite

This research report provides an overview of the R&D efforts at Educational Testing Service related to its capability for automated scoring of nonnative spontaneous speech with the SpeechRaterSM automated scoring service since its initial version was deployed in 2006. While most aspects of this R&D work have been published in various venues in recent years, no comprehensive account of the current state of SpeechRater has been provided since the initial publications following its first operational use in 2006. After a brief review of recent related work by other institutions, we summarize the main features and feature classes that have been developed and introduced into SpeechRater in the past 10 years, including features measuring aspects of pronunciation, prosody, vocabulary, grammar, content, and discourse. Furthermore, new types of filtering models for flagging nonscorable spoken responses are described, as is our new hybrid way of building linear regression scoring models with improved feature selection. Finally, empirical results for SpeechRater 5.0 (operationally deployed in 2016) are provided.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine

Chen

Zechner

Yoon

et al. 2018

ETS Research Report Series

View full text Add to dashboard Cite

show abstract

“…In our case, c0 and c1 are equal to 0.5. More details about our quantitative auditory model can be found in [14]. The following eight auditory-based features are extracted from the speech signal to perform ESR: grave/acute (G/A), open/closed (O/C), diffuse/compact (D/C), flat/sharp (F/S), mellow/strident (M/S), continuous/discontinuous (C/D), tense/lax (T/L), and mid-external energy (Wom).…”

Section: Auditory Modeling For Esrmentioning

confidence: 99%

Linear Discriminant Differential Evolution for Feature Selection in Emotional Speech Recognition

2019

View full text Add to dashboard Cite

In this paper, an evolutionary algorithm is used to select an optimal set of acoustic features for emotional speech recognition. A new algorithm that combines differential evolution (DE) optimization and linear discriminant analysis (LDA) is proposed to design an effective feature selection and classification model. An original acoustic feature framework based on auditory modeling is also presented. The auditory-based features are provided as inputs to the DE-LDA based emotional speech recognition system. To evaluate the effectiveness of the DE-LDA approach, a subset of the Emotion Prosody Speech and Transcript corpus covering five emotional states (happiness, anger, panic, sadness, and interest) is used throughout the experiments. The results show that the proposed DE-LDA model performs significantly better than the baseline systems. It achieves a classification rate of 91.6% using only 50 input parameters that are optimally selected from 128 original acoustic features.

show abstract

“…The accent of the speaker depends on his mother tongue [1,2]. The difference is negligible in respect of the speakers of the same country.…”

Section: Speaker's Accentmentioning

confidence: 99%

Dynamic Phone Warping - A Method to Measure the Distance Between Pronunciations

Babu¹,

Ramadevi²

2018

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.

show abstract

Native and non-native class discrimination using speech rhythm- and auditory-based cues

Cited by 8 publications

References 27 publications

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine

Linear Discriminant Differential Evolution for Feature Selection in Emotional Speech Recognition

Dynamic Phone Warping - A Method to Measure the Distance Between Pronunciations

Contact Info

Product

Resources

About

Native and non-native class discrimination using speech rhythm- and auditory-based cues

Cited by 8 publications

References 27 publications

Automated Scoring of Nonnative Speech Using the SpeechRaterSMv. 5.0 Engine

Automated Scoring of Nonnative Speech Using the SpeechRaterSMv. 5.0 Engine

Linear Discriminant Differential Evolution for Feature Selection in Emotional Speech Recognition

Dynamic Phone Warping - A Method to Measure the Distance Between Pronunciations

Contact Info

Product

Resources

About

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine

Automated Scoring of Nonnative Speech Using the SpeechRater^SMv. 5.0 Engine