Analysis, Modelling and Synthesis of British, Australian and American Accents

1. Analysis, Modelling and Synthesis of British, Australian and American Accents Supported by EPSRC Qin Yan Saeed Vaseghi Multimedia Communication Signal processing Lab Department of Electronic and Computer Engineering Brunel University

2. 1- Introduction to Phonetics and Acoustics of Accents 2-Research Issues in Modelling Acoustics of Accents of English 3- Current Research Problems 4- Accent Analysis and Models 5- Accent Morphing 6- Audio Demo Content

3. 1.1 Background Accents are acoustic manifestations of differences in pronunciation and intonations by a community of people from a national, regional or a socio-economic grouping. Accents are dynamic processes in that they evolve over time influenced by large-scale immigration, socio-economic changes and cultural trends. Applications of accent models include: - speech recognition, - text to speech synthesis, - voice editing, - accent morphing in broadcasting and films, - toys and computer games, - accent coaching, education. 1. Introduction to Phonetics and Acoustics of Accents

4. The importance of an accent feature depends on its distance from that of the ‘standard’ or ‘received’ pronunciation and the frequency with which that feature occurs in the acoustics of speech. • 1.2 Basic Structure of Accents • Generally the structural differences betweenaccents can be divided into two broad parts: • (a) Differences in phonetic transcriptions. • (b) Differences in acoustics correlates and intonations of accents.

5. 1.3 Phonetics of Accents • A dominant aspect of accents is in the differences in pronunciation as transcribed by a phonetic dictionary. • The differences in phonetic transcription can be categorized into two classes: • a) Differences in the number and identity of the phonemes. • For example, British English as transcribed by Cambridge University’s BEEP dictionary2 has five extra vowels: /ax(ə) ea(ɛə) ia(iə) ua (uə) ah (ɒ)/ compared to American as transcribed by Carnegie Melon University CMU dictionary. /iəɛəuə/,are allophones of /i ɛ u/.American /ɒ/ is merged with /a/ compared with British accent. • American transcription has three different levels of stress for vowels and diphthongs. Also Australian English has distinctive vowels such as /æi/ instead of /ei/ and /æƆ/for /au/. • b) Differences in phonetic realizations: phoneme substitution, deletion, insertion. • For example, ‘JOHN’ is pronounced as /ʤΛn/ in American but as /ʤƆn/in British and Australian English. The word ‘SAY’ is pronounced as /sei/ in British and American but it is pronounced as /sæi / in Australian.

6. 1.4 Acoustics of Accents • Perceived acoustics differences of accents are due to the differences, during the production of sound, in the configurations, positioning, tension and movement of laryngeal and supra-laryngeal articulatory parameters, namely vocal folds, vocal tract, tongue and lips • Four aspects of acoustic correlates of accents are considered essential for accent models and accent synthesis. These are: • (a) Formants(i.e. frequency of vocal tract resonance) correlates of accents, including: • (i) Formant trajectoriesFkj(t), k is the formant index and j is phoneme index. • (ii) Timing and magnitude of the formant target point(s) in formant space for each phonetic unit.

7. (b) Pitch prosody correlates of accents, include: (i) Pitch trajectory at various linguistic contexts and positions. e.g. pitch rise, at the beginning of a voiced group or phrase, pitch fall at the end of a phrase. (ii) Pitch nucleus i.e. the timing and magnitude of the prominent pitch event in a voiced group. (c) Duration and Timing correlates of accents, (i) Duration of vowels and diphthongs. (ii) Relative duration and timings of the two constituent vowels of diphthongs. (d) Laryngeal (glottal) correlates of accents, i.e the voice quality of speech segments in certain contexts as a function of accent.

8. 2. Research Issues in Modelling Acoustics of Accents of English • Definition of an accent ‘feature set’ composed of formants’ trajectories, formants’ target points, pitch trajectory, power trajectory, duration. • Separation, normalisation, or averaging out of speakers’ characteristics from accent characteristics, this is required for modelling parameters of accent. • Modelling formants of vowels and diphthongs, the latter is composed of two connected elementary sounds. • Modelling the duration of vowels and diphthongs and the relative duration of the two halves of diphthongs. • Modelling pitch trajectory in different phonetic/linguistic positions and contexts. • Modelling voice quality correlates of an accents in different phonetic/linguistic positions and contexts. • Integration of all accent features within a coherent generative model.

9. Accent Profile (AP) Parameters Comments Rank Phonetic Parameters Substitution, insertion, deletion Pronunciation differences obtained from phonetic transcription dictionaries ***** Supra-laryngeal and Laryngeal Correlates Formants & their trajectories 2nd formant with largest variance is most sensitive to accent **** Glottal pulse (Voice Quality) Durations and shapes of opening and closing of glottal folds ** Prosody Correlates F0 mean Average of pitch * F0 range Range of pitch * Pitch Nucleus Prominent point (stressed) within an intonation group (Tone Unit) *** Initial Pitch Rise First pitch slope of a narrative utterance *** Final Pitch Lowering Final fall pitch slope of a narrative utterance *** Final Pitch Rise Final rise pitch slope of a narrative utterance *** Timing and Delivery Correlates Speaking Rate Phonemes or words per second * Phoneme Duration Vowel duration elongation and complete pronunciation all affect *** Excessive Co-articulation Clipped or short duration sounds ****

10. Speech Accent Feature Analysis Method Speaking Rate & Durations HMM Training Labeling & Segmentation Formants & Trajectories Input Speech Accent Profile F0 Range/Mean Pitch Accents Pitch Contour Tracker Pitch Marker Tone Nucleus Features Block diagram illustration of the processes involved in accent analysis • The basic processes involved in accent analysis includes • Speech phoneticlabelling and boundary segmentationusing HMMs • Pitch trajectory and pitch nucleus estimation • Formant models and formant track estimation • Duration and power trajectory analysis

11. Analysisof Duration Correlate of AU, US and UK Accent Speech Figure: Comparison of speaking rates of British, Australian and American. 0.2 0.18 0.16 0.14 0.12 Duration (sec) 0.1 0.08 0.06 0.04 Australian British American 0.02 aa ae ah ao aw ay eh er ey ih iy ow oy uh uw Figure: Comparison of phoneme durations of British, Australian and American.

12. Speaking Rate (number/sec) Phone Word British 12.1 3.64 American 11.6 3.1 Australian 10.8 2.8 Comparison of speaking rates of British, American and Australian Accents. • Australian speaking (word) rate is 23% slower than British • American speaking (word) rate is 15% slower than British Table : (%) word error of speech recognition across British, American and Australian accents. • There is an apparent correlation between automatic speech recognition and speaking rate. • Australian with the slowest speaking rate obtains the best recognition results followed by American and British.

13. Formant Estimation with 2D-HMM • Formant feature extraction, illustrated consists of three main functions, • an LP model, • (2) a polynomial root finder, and • (3) a contour trend estimator. • Consider the z-transfer function of an LP model with K real poles and I complex pole pairs and a gain factor G as • where Ak is the pole radius, Fi the pole frequency and Fs sampling frequency. D-estimator Formant candidate Feature vector Frequency,Bandwidth Intensity Calculation LPC Model Polynomial roots Segmentation & window Speech LP-based Formant-candidate feature extraction method

14. Time(s) Frequency(Hz) Illustration of of LP spectrum and the modelling of 6 complex pole pairs of a speech segment with an HMM composed of 4 formant-states. • 2D HMMs span time and frequency dimensions • Left-right HMM states across frequency model formants such that the first state models the first formant, the second state the second formant and so on • The distribution of formants in each state is modelled by a mixture Gaussian density.

15. Three spectrogram examples of formant tracks superimposed on LPC spectrum of speech

16. Comparison of histograms (thin solid line) and Gaussian HMMs of formants of Australian English (bold dashed line). X axis: frequency (Hz); Y axis: probability. The figures show that HMMS are excellent models of the distribution of the formants.

17. Comparison of Formants Spaces of American, Australian and British Accents F1 vs F2 space of British, Australian and American English. Click phoneme to listen. • Note the following features: • Rising of vowels /ae/ and /eh/ in Australian. • Fronting of the open vowel /aa/ and high vowel /uw/ in Australian. • Fronting and rising of the vowel /er/ in Australian. • The vowels /iy/, /eh/ and /ae/ in Australian are closer.

18. Figure : Comparison of trajectories and target time of formant of British,Australian and American accents

19. Figure : Comparison of formants of Australian, British and American (female) Formant Ranking using a normalised distance • 2nd Formant has widest frequency range and is most sensitive to Accent

20. Accent Morphing Method Accent Model HMMTraining/ Adaptation Speech Labeling & Segmentation Prosody Modification Source Speech Formant Mapping Formant Estimation Accent Synthesised Speech Pitch Tracker Figure : Diagram of a voice morphing system used for accent conversion • Formant Mapping : Transformation of formants of the source towards those of the target accent is based on non-uniform linear prediction modelfrequency warping. • Prosody Modification : based on time domain pitch synchronous overlap and add (TD-PSOLA) method. • Prosody Modification includes pitch slope, duration and power trajectory. • Application : Text to speech synthesis, Broadcasting System e.g. Accent modification in films, Education software such language teaching, Speech interface in mobile, Call centre and other electronic products

21. Formant Transformation via Non-Uniform LP Frequency Warping -35 -40 -45 -50 -55 Magnitude (dB) -60 -65 I 12 -70 I I 23 34 -75 1 0.1 BW BW BW 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 BW 1 3 4 2 F F F F F 01 12 23 34 45 Frequency (Hz) Figure Illustration of a non-uniform frequency warping using LP model frequency response. The spectrum is divided into a number of bands centered on the formants and a different set of warping parameters is applied to each band. Formant Transformation Ratios Linear Prediction Model Polynomial roots Pole estimation Accent modified spectrum LP Spectrum Mapping Speech Formant Estimation Formant HMMs Figure : Illustration modification of spectrum towards formants of target accent

22. The frequency bands of the source speaker [F01F12F23F34F45] are mapped to the target accent using a set of warping ratios derived from differences in the formants of phonetic segments of speech across accents as - T T f f + 1 i i a = + ( 1 ) i i - S S f f + 1 i i Where fiT and fiS are the ith formants of the source and target accents The frequency mapping can be expressed as = a f f + + + ( 1 ) ( 1 ) i i i i ( 1 ) i i Figure : Illustration of warped(solid line) and original(dash dot line) formant trajectories of /aa/ in accent conversion from Australian to British.

23. Pitch Modification Using Time Domain PSOLA (TD-PSOLA) marks Source pitch marks Target pitch Illustration of mapping of pitch periods of a source speech to a target Source Speech Pitch Marks Target Speech Pitch Marks • TD-PSOLA is applied into each corresponding voiced speech segment to modify the pitch slope and duration of the segments

24. Examples of changes in accent/duration modulation of pitch (b) (a) (a) ‘article’ in Australian, (b) Australian-accent ‘article’ transformed to British accent (d) (c) (c) ‘asked’ in Australian, (d) Australian-accent ‘article’ transformed to British accent

25. Source Source Speech Speech LP LP Model Model Source Source Formant Formant Speaker Speaker Trajectory Trajectory HMM HMM Mapped Mapped Model Model Spectrum Warping / Pole Rotation Speech Speech Speech Speech Warping Warping Recon Recon- Spectrum Warping / Pole Rotation Factors Factors struction struction Target Target Formant Formant Speaker Speaker Trajectory Trajectory HMM HMM Model Model - - LP LP LPC LPC Model Model Target Target Speech Speech Model Model Formant Formant Speech Speech Formant Mapping Formant Mapping Estimation Estimation Tracking Tracking Reconstruction Reconstruction An Outline of Voice-Morph: A system for Voice and Accent Conversion An example of voice conversion American male Transformed(AM m->f) American female

26. Accent Conversion Demonstration Source Accent TargetAccent Spoken word Transformed Australian British ‘Article’ ‘Claim’ ‘Beige’ British American Transformed ‘Cooperation’ ‘Boston’ ‘Opposition’ ‘The occupied’

27. The End