Classification of Malaysian vowels using formant based features

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software, especially using English as the language of choice. Despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. In this paper, a new feature based on formant is presented and evaluated on Malaysian spoken vowels. These features were classified and used to identify vowels recorded from 80 Malaysian speakers. A back propagation neural network (BPNN) model was developed to classify the vowels. Six formant features were evaluated, which were the first three formant frequencies and the distances between each of them. Results, showed that overall vowel classification rate of these three formant combinations are comparatively the same but differs in terms of individual vowel classification

Exploring British accents : modelling the trap–bath split with functional data analysis

The sound of our speech is influenced by the places we come from. Great Britain contains a wide variety of distinctive accents which are of interest to linguistics. In particular, the ‘a’ vowel in words like ‘class’ is pronounced differently in the North and the South. Speech recordings of this vowel can be represented as formant curves or as mel‐frequency cepstral coefficient curves. Functional data analysis and generalised additive models offer techniques to model the variation in these curves. Our first aim was to model the difference between typical Northern and Southern vowels /æ/ and /ɑ/, by training two classifiers on the North‐South Class Vowels dataset collected for this paper. Our second aim is to visualise geographical variation of accents in Great Britain. For this we use speech recordings from a second dataset, the British National Corpus (BNC) audio edition. The trained models are used to predict the accent of speakers in the BNC, and then we model the geographical patterns in these predictions using a soap film smoother. This work demonstrates a flexible and interpretable approach to modelling phonetic accent variation in speech recordings

The effects of English proficiency on the processing of Bulgarian-accented English by Bulgarian-English bilinguals

This dissertation explores the potential benefit of listening to and with one’s first-language accent, as suggested by the Interspeech Intelligibility Benefit Hypothesis (ISIB). Previous studies have not consistently supported this hypothesis. According to major second language learning theories, the listener’s second language proficiency determines the extent to which the listener relies on their first language phonetics. Hence, this thesis provides a novel approach by focusing on the role of English proficiency in the understanding of Bulgarian-accented English for Bulgarian-English bilinguals. The first experiment investigated whether evoking the listeners’ L1 Bulgarian phonetics would improve the speed of processing Bulgarian-accented English words, compared to Standard British English words, and vice versa. Listeners with lower English proficiency processed Bulgarian-accented English faster than SBE, while high proficiency listeners tended to have an advantage with SBE over Bulgarian accent. The second experiment measured the accuracy and reaction times (RT) in a lexical decision task with single-word stimuli produced by two L1 English speakers and two Bulgarian-English bilinguals. Listeners with high proficiency in English responded slower and less accurately to Bulgarian-accented speech compared to L1 English speech and compared to lower proficiency listeners. These accent preferences were also supported by the listener’s RT adaptation across the first experimental block. A follow-up investigation compared the results of L1 UK English listeners to the bilingual listeners with the highest proficiency in English. The L1 English listeners and the bilinguals processed both accents with similar speed, accuracy and adaptation patterns, showing no advantage or disadvantage for the bilinguals. These studies support existing models of second language phonetics. Higher proficiency in L2 is associated with lesser reliance on L1 phonetics during speech processing. In addition, the listeners with the highest English proficiency had no advantage when understanding Bulgarian-accented English compared to L1 English listeners, contrary to ISIB. Keywords: Bulgarian-English bilinguals, bilingual speech processing, L2 phonetic development, lexical decision, proficienc

Statistical Parametric Methods for Articulatory-Based Foreign Accent Conversion

Foreign accent conversion seeks to transform utterances from a non-native speaker (L2) to appear as if they had been produced by the same speaker but with a native (L1) accent. Such accent-modified utterances have been suggested to be effective in pronunciation training for adult second language learners. Accent modification involves separating the linguistic gestures and voice-quality cues from the L1 and L2 utterances, then transposing them across the two speakers. However, because of the complex interaction between these two sources of information, their separation in the acoustic domain is not straightforward. As a result, vocoding approaches to accent conversion results in a voice that is different from both the L1 and L2 speakers. In contrast, separation in the articulatory domain is straightforward since linguistic gestures are readily available via articulatory data. However, because of the difficulty in collecting articulatory data, conventional synthesis techniques based on unit selection are ill-suited for accent conversion given the small size of articulatory corpora and the inability to interpolate missing native sounds in L2 corpus. To address these issues, this dissertation presents two statistical parametric methods to accent conversion that operate in the acoustic and articulatory domains, respectively. The acoustic method uses a cross-speaker statistical mapping to generate L2 acoustic features from the trajectories of L1 acoustic features in a reference utterance. Our results show significant reductions in the perceived non-native accents compared to the corresponding L2 utterance. The results also show a strong voice-similarity between accent conversions and the original L2 utterance. Our second (articulatory-based) approach consists of building a statistical parametric articulatory synthesizer for a non-native speaker, then driving the synthesizer with the articulators from the reference L1 speaker. This statistical approach not only has low data requirements but also has the flexibility to interpolate missing sounds in the L2 corpus. In a series of listening tests, articulatory accent conversions were rated more intelligible and less accented than their L2 counterparts. In the final study, we compare the two approaches: acoustic and articulatory. Our results show that the articulatory approach, despite the direct access to the native linguistic gestures, is less effective in reducing perceived non-native accents than the acoustic approach