445 research outputs found
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.Comment: to appear in Computer Speech & Language -
https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial
text overlap with arXiv:1807.1094
Native Speaker Perceptions of Accented Speech: The English Pronunciation of Macedonian EFL Learners
The paper reports on the results of a study that aimed to describe the vocalic and consonantal features of the English pronunciation of Macedonian EFL learners as perceived by native speakers of English and to find out whether native speakers who speak different standard variants of English perceive the same segments as non-native. A specially designed computer web application was employed to gather two types of data: a) quantitative (frequency of segment variables and global foreign accent ratings on a 5-point scale), and b) qualitative (open-ended questions). The result analysis points out to three most frequent markers of foreign accent in the English speech of Macedonian EFL learners: final obstruent devoicing, vowel shortening and substitution of English dental fricatives with Macedonian dental plosives. It also reflects additional phonetic aspects poorly explained in the available reference literature such as allophonic distributional differences between the two languages and intonational mismatch
An exploration of the rhythm of Malay
In recent years there has been a surge of interest in speech rhythm. However we still lack a clear understanding of the nature of rhythm and rhythmic differences across languages. Various metrics have been proposed as means for measuring rhythm on the phonetic level and making typological comparisons between languages (Ramus et al, 1999; Grabe & Low, 2002; Dellwo, 2006) but the debate is ongoing on the extent to which these metrics capture the rhythmic basis of speech (Arvaniti, 2009; Fletcher, in press). Furthermore, cross linguistic studies of rhythm have covered a relatively small number of languages and research on previously unclassified languages is necessary to fully develop the typology of rhythm. This study examines the rhythmic features of Malay, for which, to date, relatively little work has been carried out on aspects rhythm and timing.
The material for the analysis comprised 10 sentences produced by 20 speakers of standard Malay (10 males and 10 females). The recordings were first analysed using rhythm metrics proposed by Ramus et. al (1999) and Grabe & Low (2002). These metrics (∆C, %V, rPVI, nPVI) are based on durational measurements of vocalic and consonantal intervals. The results indicated that Malay clustered with other so-called syllable-timed languages like French and Spanish on the basis of all metrics. However, underlying the overall findings for these metrics there was a large degree of variability in values across speakers and sentences, with some speakers having values in the range typical of stressed-timed languages like English.
Further analysis has been carried out in light of Fletcher’s (in press) argument that measurements based on duration do not wholly reflect speech rhythm as there are many other factors that can influence values of consonantal and vocalic intervals, and Arvaniti’s (2009) suggestion that other features of speech should also be considered in description of rhythm to discover what contributes to listeners’ perception of regularity. Spectrographic analysis of the Malay recordings brought to light two parameters that displayed consistency and regularity for all speakers and sentences: the duration of individual vowels and the duration of intervals between intensity minima.
This poster presents the results of these investigations and points to connections between the features which seem to be consistently regulated in the timing of Malay connected speech and aspects of Malay phonology. The results are discussed in light of current debate on the descriptions of rhythm
Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method
The automatic identification and analysis of pronunciation errors, known as
Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer
Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning
or speech therapy applications. Existing MDD methods relying on analysing
phonemes can only detect categorical errors of phonemes that have an adequate
amount of training data to be modelled. With the unpredictable nature of the
pronunciation errors of non-native or disordered speakers and the scarcity of
training datasets, it is unfeasible to model all types of mispronunciations.
Moreover, phoneme-level MDD approaches have a limited ability to provide
detailed diagnostic information about the error made. In this paper, we propose
a low-level MDD approach based on the detection of speech attribute features.
Speech attribute features break down phoneme production into elementary
components that are directly related to the articulatory system leading to more
formative feedback to the learner. We further propose a multi-label variant of
the Connectionist Temporal Classification (CTC) approach to jointly model the
non-mutually exclusive speech attributes using a single model. The pre-trained
wav2vec2 model was employed as a core model for the speech attribute detector.
The proposed method was applied to L2 speech corpora collected from English
learners from different native languages. The proposed speech attribute MDD
method was further compared to the traditional phoneme-level MDD and achieved a
significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR),
and Diagnostic Error Rate (DER) over all speech attributes compared to the
phoneme-level equivalent
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