465 research outputs found
ACCDIST: A Metric for comparing speakers' accents
This paper introduces a new metric for the quantitative assessment of the similarity of speakers' accents. The ACCDIST metric is based on the correlation of inter-segment distance tables across speakers or groups. Basing the metric on segment similarity within a speaker ensures that it is sensitive to the speaker's pronunciation system rather than to his or her voice characteristics. The metric is shown to have an error rate of only 11% on the accent classification of speakers into 14 English regional accents of the British Isles, half the error rate of a metric based on spectral information directly. The metric may also be useful for cluster analysis of accent groups
Formant frequencies of vowels in 13 accents of the British Isles
International audienceThis study is a formant-based investigation of the vowels of male speakers in 13 accents of the British Isles. It provides F1/F2 graphs (obtained with a semi-automatic method) which could be used as starting points for more thorough analyses. The article focuses on both phonetic realization and systemic phenomena, and it also provides detailed information on automatic formant measurements. The aim is to obtain an up-to-date picture of within-and between-accent vowel variation in the British Isles. F1/F2 graphs plot z-scored Bark-transformed formant frequencies, and values in Hertz are also provided. Along with the findings, a number of methodological issues are addressed
Acoustic model selection using limited data for accent robust speech recognition
This paper investigates techniques to compensate for the effects of regional accents of British English on automatic speech recognition (ASR) performance. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation, or to use accent identification (AID) to identify the speakerâs accent followed by accent-dependent ASR? Three approaches to accent-dependent
modelling are investigated: using the âcorrectâ accent model, choosing a model using supervised (ACCDIST-based) accent identifi- cation (AID), and building a model using data from neighbouring
speakers in âAID spaceâ. All of the methods outperform the accentindependent model, with relative reductions in ASR error rate of up to 44%. Using on average 43s of speech to identify an appropriate
accent-dependent model outperforms using it for supervised speaker-adaptation, by 7%
An integrated dialect analysis tool using phonetics and acoustics
This study aimed to verify a computational phonetic and acoustic analysis tool created in the MATLAB environment. A dataset was obtained containing 3 broad American dialects (Northern, Western and New England) from the TIMIT database using words that also appeared in the Swadesh list. Each dialect consisted of 20 speakers uttering 10 sentences. Verification using phonetic comparisons between dialects was made by calculating the Levenshtein distance in Gabmap and the proposed software tool. Agreement between the linguistic distances using each analysis method was found. Each tool showed increasing linguistic distance as a function of increasing geographic distance, in a similar shape to Seguy's curve. The proposed tool was then further developed to include acoustic characterisation capability of inter dialect dynamics. Significant variation between dialects was found for the pitch, trajectory length and spectral rate of change for 7 of the phonetic vowels investigated. Analysis of the vowel area using the 4 corner vowels indicated that for male speakers, geographically closer dialects have smaller variations in vowel space area than those further apart. The female utterances did not show a similar pattern of linguistic distance likely due to the lack of one corner vowel /u/, making the vowel space a triangle
The impact of voice on trust attributions
Trust and speech are both essential aspects of human interaction. On the one hand, trust
is necessary for vocal communication to be meaningful. On the other hand, humans have
developed a way to infer someoneâs trustworthiness from their voice, as well as to signal their
own. Yet, research on trustworthiness attributions to speakers is scarce and contradictory,
and very often uses explicit data, which do not predict actual trusting behaviour. However,
measuring behaviour is very important to have an actual representation of trust. This thesis
contains 5 experiments aimed at examining the influence of various voice characteristics â
including accent, prosody, emotional expression and naturalness â on trusting behaviours
towards virtual players and robots. The experiments have the "investment game"âa method
derived from game theory, which allows to measure implicit trustworthiness attributions over
time â as their main methodology. Results show that standard accents, high pitch, slow
articulation rate and smiling voice generally increase trusting behaviours towards a virtual
agent, and a synthetic voice generally elicits higher trustworthiness judgments towards
a robot. The findings also suggest that different voice characteristics influence trusting
behaviours with different temporal dynamics. Furthermore, the actual behaviour of the
various speaking agents was modified to be more or less trustworthy, and results show
that peopleâs trusting behaviours develop over time accordingly. Also, people reinforce
their trust towards speakers that they deem particularly trustworthy when these speakers
are indeed trustworthy, but punish them when they are not. This suggests that peopleâs
trusting behaviours might also be influenced by the congruency of their first impressions
with the actual experience of the speakerâs trustworthiness â a "congruency effect". This
has important implications in the context of HumanâMachine Interaction, for example for
assessing usersâ reactions to speaking machines which might not always function properly.
Taken together, the results suggest that voice influences trusting behaviour, and that first
impressions of a speakerâs trustworthiness based on vocal cues might not be indicative of
future trusting behaviours, and that trust should be measured dynamically
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, di�erent accents of a language exhibit more
fine-grained di�erences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insu�cient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
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