Extraction of vocal-tract system characteristics from speechsignals

We propose methods to track natural variations in the characteristics of the vocal-tract system from speech signals. We are especially interested in the cases where these characteristics vary over time, as happens in dynamic sounds such as consonant-vowel transitions. We show that the selection of appropriate analysis segments is crucial in these methods, and we propose a selection based on estimated instants of significant excitation. These instants are obtained by a method based on the average group-delay property of minimum-phase signals. In voiced speech, they correspond to the instants of glottal closure. The vocal-tract system is characterized by its formant parameters, which are extracted from the analysis segments. Because the segments are always at the same relative position in each pitch period, in voiced speech the extracted formants are consistent across successive pitch periods. We demonstrate the results of the analysis for several difficult cases of speech signals

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Can phonation types be reliably measured from sound spectra? Some data from Wa and Burmese

This paper assesses the value of measuring aspects of an unmodified acoustic recordings of speech in the two language Burmese (Tibeto-Burman) and Wa (Mon_Khmer) in relation to the glottal source, or phonation type. This method faces the problem of how to ensure that what is measured is indeed attributable to the glottal source andnot to supralaryngeal acoustic shaping, or vowel quality. The methods adopted include: analysis of the relative prominence of the H1 and H2, formant amplitude and spectral tilt. The findings are that in Wa H2, F1 and F2 are all more energetic than H1 to a greater degree in creaky phonation than in breathy, though this is due in part to the significantly dominant H1 in breathy phonation. For Burmese, the methods in this study are too crude to tell these two phonation types apart, but they are sufficient to identify the cruder three-way categorisation of phonation types (modal, creaky and breathy), which, it has been suggested, is sufficient to give a satisfactory account of phonologically contrastive phonation type for most purposes. The findings suggest further that the relationship between the higher frequency region of the spectrum and phonation type merits further investigation

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Analysing Changes in the Acoustic Features of the Human Voice to Detect Depression amongst Biological Females in Higher Education

Depression significantly affects a large percentage of the population, with young adult females being one of the most at-risk demographics. Concurrently, there is a growing demand on healthcare, and with sufficient resources often unavailable to diagnose depression, new diagnostic methods are needed that are both cost-effective and accurate. The presence of depression is seen to significantly affect certain acoustic features of the human voice. Acoustic features have been found to exhibit subtle changes beyond the perception of the human auditory system when an individual has depression. With advances in speech processing, these subtle changes can be observed by machines. By measuring these changes, the human voice can be analysed to identify acoustic features that show a correlation with depression. The implementation of voice diagnosis would both reduce the burden on healthcare and ensure those with depression are diagnosed in a timely fashion, allowing them quicker access to treatment. The research project presents an analysis of voice data from 17 biological females between the ages of 20-26 years old in higher education as a means to detect depression. Eight participants were considered healthy with no history of depression, whilst the other nine currently had depression. Participants performed two vocal tasks consisting of extending sounds for a period of time and reading back a passage of speech. Six acoustic features were then measured from the voice data to determine whether these features can be utilised as diagnostic indicators of depression. The main finding of this study demonstrated one of the acoustic features measured demonstrates significant differences when comparing depressed and healthy individuals.<br/