Digital processing of speech signals

Numerical Methods for Scientists and Engineers

1. Word Frequencies. 2. Non-parametric models. 3. Parametric models. 4. Mixture distributions. 5. The Randomness Assumption. 6. Examples of Applications. A. List of Symbols. B. Solutions of the exercises. C. Software. D. Data sets. Bibliography. Index.

Word Frequency Distributions

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as ‘spectrum’ or ‘autisms’ reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case–control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being ‘feature-rich’, big data should be both ‘broad’ (i.e., large sample size) and ‘deep’ (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model’s utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with ‘supervised’ models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

Big data approaches to decomposing heterogeneity across the autism spectrum

This paper describes the acquisition of a new database of dysarthric speech in terms of aligned acoustics and articulatory data This database currently includes data from seven individuals with speech impediments caused by cerebral palsy or amyotrophic lateral sclerosis and age- and gender-matched control subjects Each of the individuals with speech impediments are given standardized assessments of speech-motor function by a speech-language pathologist Acoustic data is obtained by one head-mounted and one directional microphone Articulatory data is obtained by electromagnetic articulography, which allows the measurement of the tongue and other articulators during speech, and by 3D reconstruction from binocular video sequences The stimuli are obtained from a variety of sources including the TIMIT database, lists of identified phonetic contrasts, and assessments of speech intelligibility This paper also includes some analysis as to how dysarthric speech differs from non-dysarthric speech according to features such as length of phonemes, and pronunciation errors

/pdf/the-torgo-database-of-acoustic-and-articulatory-speech-from-4zh3zrs0ga.pdf

The TORGO database of acoustic and articulatory speech from speakers with dysarthria

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

Dysarthria is a motor speech impairment affecting millions of people. Dysarthric speech can be far less intelligible than that of non-dysarthric speakers, causing significant communication difficulties. The goal of our work is to understand the effect that certain modifications have on the intelligibility of dysarthric speech. These modifications are designed to identify aspects of the speech signal or signal processing that may be especially relevant to the effectiveness of a system that transforms dysarthric speech to improve its intelligibility. A result of this study is that dysarthric speech can, in the best case, be modified only at the short-term spectral level to improve intelligibility from 68% to 87%. A baseline transformation system using standard technology, however, does not show improvement in intelligibility. Prosody also has a significant (p<0.05) effect on intelligibility.

Intelligibility of modifications to dysarthric speech

A new formant-tracking algorithm using phoneme information is proposed. Conventional formant-tracking algorithms obtain formant tracks by analyzing the acoustic speech signal using continuity constraints without any additional information. The formant-tracking error rate of the conventional methods is reportedly in the range of 10%-20%. In this paper, we show that if text or phoneme transcription of speech utterances is available, the error rate can be significantly reduced. The basic idea behind this approach is that given the phoneme identity, formant-tracking algorithms can have a better clue of where to look for formants. The algorithm consists of three phases: 1) analysis, 2) segmentation and alignment, and 3) formant tracking by the Viterbi searching algorithm. In the analysis phase, formant candidates are obtained for each analysis frame by solving the linear prediction polynomial. In the segmentation and alignment phase, the text corresponding to the input speech utterance is converted into a sequence of phoneme symbols. Then, the phoneme sequence is time aligned with the speech utterance. A hidden Markov model (HMM) based automatic segmentation algorithm is used for forced-time alignment. For each phoneme segment, nominal formant frequencies are assigned at the center of each phoneme segment. Then nominal formant tracks for the entire utterance are obtained by interpolating the nominal formant frequencies. In order to compensate for the coarticulation effect, different interpolation methods are used depending on the phonemic context. The interpolation process makes the formant-tracking algorithm robust to possible segmentation errors made by the HMM-based segmentation algorithm. As a result, the proposed formant-tracking algorithm does not require highly accurate alignment/segmentation. Finally, a set of formants is chosen from the formant candidates in such a way that the resulting formant tracks come close to the nominal formant tracks while satisfying the continuity constraints. The algorithm is tested using natural speech utterances and the performance is compared against formant tracks obtained by the conventional method using continuity constraints only. The new algorithm significantly reduces the formant-tracking error rate (5.03% for male and 3.73% for female) over the conventional formant-tracking algorithm (13.00% for male and 15.82% for female).

/pdf/formant-tracking-using-context-dependent-phonemic-2tw3mtggu4.pdf

Formant tracking using context-dependent phonemic information

One of the major problems in concatenative synthesis is the occurrence of audible discontinuities between two successive concatenative units. Several studies have attempted to discover objective distance measures that predict the audibility of these discontinuities. In this paper, we investigate mid-vowel joins for three vowels with a range of post-vocalic consonant contexts typical for diphone databases. A first perceptual experiment uses a pairwise comparison procedure to find two subsets of unit combinations: Those with versus without audible discontinuities. A second perceptual experiment uses these two subsets in a procedure where formant resynthesis is used to manipulate three sources of discontinuity separately: formant frequencies, formant bandwidths, and overall energy. Results show mismatch in formant frequencies provides the largest contribution to audible discontinuity, followed by mismatch in overall energy

The Contribution of Various Sources of Spectral Mismatch to Audible Discontinuities in a Diphone Database

In this paper, we investigate the predictive power of different prosodic factorization schemes with respect to pitch movement. We use this to propose an extension of a standard diphone database with diphones that have been recorded in different prosodic contexts. The goal of this research is to reduce the amount of pitch modification required, thereby improving the segmental quality of the synthetic voice. We present a factorization scheme based on the foot structure of utterances and show that this efficient scheme results in a fairly small number of additional diphones that need to be recorded.

J. van Santen

Papers

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

Intelligibility of modifications to dysarthric speech

Formant tracking using context-dependent phonemic information

The Contribution of Various Sources of Spectral Mismatch to Audible Discontinuities in a Diphone Database

Prosodic factors for predicting local pitch shape