A Deep Generative Model of Vowel Formant Typology

What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information -- the first two formant values -- rather than modeling discrete sets of phonemic symbols (IPA). We develop a novel generative probability model and report results based on a corpus of 233 languages.Comment: NAACL 201

Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction

Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions

The unexplained nature of reading.

The effects of properties of words on their reading aloud response times (RTs) are 1 major source of evidence about the reading process. The precision with which such RTs could potentially be predicted by word properties is critical to evaluate our understanding of reading but is often underestimated due to contamination from individual differences. We estimated this precision without such contamination individually for 4 people who each read 2,820 words 50 times each. These estimates were compared to the precision achieved by a 31-variable regression model that outperforms current cognitive models on variance-explained criteria. Most (around 2/3) of the meaningful (non-first-phoneme, non-noise) word-level variance remained unexplained by this model. Considerable empirical and theoretical-computational effort has been expended on this area of psychology, but the high level of systematic variance remaining unexplained suggests doubts regarding contemporary accounts of the details of the mechanisms of reading at the level of the word. Future assessment of models can take advantage of the availability of our precise participant-level database

Analysis of Vocal Disorders in a Feature Space

This paper provides a way to classify vocal disorders for clinical applications. This goal is achieved by means of geometric signal separation in a feature space. Typical quantities from chaos theory (like entropy, correlation dimension and first lyapunov exponent) and some conventional ones (like autocorrelation and spectral factor) are analysed and evaluated, in order to provide entries for the feature vectors. A way of quantifying the amount of disorder is proposed by means of an healthy index that measures the distance of a voice sample from the centre of mass of both healthy and sick clusters in the feature space. A successful application of the geometrical signal separation is reported, concerning distinction between normal and disordered phonation.Comment: 12 pages, 3 figures, accepted for publication in Medical Engineering & Physic

Predicting Speech Intelligibility

Hearing impairment, and specifically sensorineural hearing loss, is an increasingly prevalent condition, especially amongst the ageing population. It occurs primarily as a result of damage to hair cells that act as sound receptors in the inner ear and causes a variety of hearing perception problems, most notably a reduction in speech intelligibility. Accurate diagnosis of hearing impairments is a time consuming process and is complicated by the reliance on indirect measurements based on patient feedback due to the inaccessible nature of the inner ear. The challenges of designing hearing aids to counteract sensorineural hearing losses are further compounded by the wide range of severities and symptoms experienced by hearing impaired listeners. Computer models of the auditory periphery have been developed, based on phenomenological measurements from auditory-nerve fibres using a range of test sounds and varied conditions. It has been demonstrated that auditory-nerve representations of vowels in normal and noisedamaged ears can be ranked by a subjective visual inspection of how the impaired representations differ from the normal. This thesis seeks to expand on this procedure to use full word tests rather than single vowels, and to replace manual inspection with an automated approach using a quantitative measure. It presents a measure that can predict speech intelligibility in a consistent and reproducible manner. This new approach has practical applications as it could allow speechprocessing algorithms for hearing aids to be objectively tested in early stage development without having to resort to extensive human trials. Simulated hearing tests were carried out by substituting real listeners with the auditory model. A range of signal processing techniques were used to measure the model’s auditory-nerve outputs by presenting them spectro-temporally as neurograms. A neurogram similarity index measure (NSIM) was developed that allowed the impaired outputs to be compared to a reference output from a normal hearing listener simulation. A simulated listener test was developed, using standard listener test material, and was validated for predicting normal hearing speech intelligibility in quiet and noisy conditions. Two types of neurograms were assessed: temporal fine structure (TFS) which retained spike timing information; and average discharge rate or temporal envelope (ENV). Tests were carried out to simulate a wide range of sensorineural hearing losses and the results were compared to real listeners’ unaided and aided performance. Simulations to predict speech intelligibility performance of NAL-RP and DSL 4.0 hearing aid fitting algorithms were undertaken. The NAL-RP hearing aid fitting algorithm was adapted using a chimaera sound algorithm which aimed to improve the TFS speech cues available to aided hearing impaired listeners. NSIM was shown to quantitatively rank neurograms with better performance than a relative mean squared error and other similar metrics. Simulated performance intensity functions predicted speech intelligibility for normal and hearing impaired listeners. The simulated listener tests demonstrated that NAL-RP and DSL 4.0 performed with similar speech intelligibility restoration levels. Using NSIM and a computational model of the auditory periphery, speech intelligibility can be predicted for both normal and hearing impaired listeners and novel hearing aids can be rapidly prototyped and evaluated prior to real listener tests