We present a real-coded genetic algorithm that efficiently estimates parameters of a formant trajectory model. The genetic algorithm uses roulette-wheel selection and elitism to minimize the root mean square error between the observed formant trajectory and the model trajectory. Parameters, including vowel and consonant target values and coarticulation parameters, are estimated for a corpus of English clear and conversational CVC words. Results show consistent consonant formant targets, even when those consonants do not themselves have formant structure. We also present findings of a relationship between a coarticulation parameter and the consonant identity. Index Terms: coarticulation, formant targets, genetic algorithms. 1.

Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.

This paper presents the results of formant analysis using a newly developed formant contour model. We model formant contours with a linear combination of formant target values and coarticulation functions for /wVl/ and /tVl/ words. While formant target values are estimated globally over different speaking styles, coarticulation coefficients are estimated for individual tokens. The results show that the estimated coarticulation coefficients are inherently different between clear (CLR) and conversational (CNV) speech and that the movement of articulators when producing CLR speech is faster than when producing CNV speech. On the other hand, speaking rate is not a key determinant in movement of articulators at vowel onsets. The direct measure of F2 slope is strongly correlated with estimated coarticulation coefficients, which may lead to less parameters to estimate.

Previous work on formant targets has assumed that these targets are independent of the speaking style. In this paper, we estimate consonant and vowel targets in a database of “clear” and “conversational” speech, using both style-independent and style-dependent models. The test-set errors and clustering of the estimated target values indicate that for this corpus, formant targets depend on the speaking style. Vowel classification accuracy was then tested on estimated target values and compared with classification based on observed formant values. Tokenbased style-independent classification shows greater accuracy for conversational speech (82.19%) than observed-value classification (73.97%). Index Terms: formant target, clear speech, formant contour model.

This paper describes a system for assistive writing, the Speech Supplemented Word Prediction Program (SSWPP). This system uses the first letter of a word typed by the user as well as the user’s (possibly low-intelligibility) speech to predict the intended word. The ASR system, which is the focus of this paper, is a speaker-dependent isolated-word recognition system. Word-level results from a non-dysarthric speaker indicate that almost all errors could be corrected by the SSWPP language model. Results from five speakers with moderate to severe dysarthria (average intelligibility 61.7%) averaged 62 % for word recognition and 65 % for out-of-vocabulary identification. Index Terms: assistive communication, dysarthria, automatic speech recognition

We examined how much listeners can benefit from listening to ldquoclearrdquo (CLR) speech compared to ldquoconversationalrdquo (CNV) speech, both spoken at different speaking rates. Vowel intelligibilities of four front vowels (/i:/, /I/, /E/, and /ei/) in background noise were measured with four speaking styles (CNV/SLOW, CNV, CLR, and CLR/FAST). Results showed only tense vowels of CLR speech had a significant difference between CNV and CLR speaking styles, after energy and F0 contour were normalized. We synthesized hybrid (HYB) speech whose formant features were equal to those of CLR speech, while all other features were taken from CNV speech. Primary conclusions from this study are (1) naturally-spoken fast CLR speech was not as intelligible as CLR speech, (2) enhancing formant frequencies to resemble those of CLR speech was effective at improving vowel intelligibility, and (3) spectral tilt and formant bandwidths were not contributing factors to the CLR speech benefit.

This paper reports an investigation of features relevant for classifying two speaking styles, namely, conversational speaking style and clear (e.g. hyper-articulated) speaking style. Spectral and prosodic features were automatically extracted from speech and classified using decision tree classifiers and multilayer perceptrons to achieve accuracies of about 71 % and 77% respectively. More interestingly, we found that out of the 56 features only about 9 features are needed to capture the most predictive power. While perceptual studies have shown that spectral cues are more useful than prosodic features for intelligibility [1], here we find prosodic features are more important for classification. Index Terms: binary classification, acoustic features, decision tree classifier, multilayer perceptron,

Alzheimer’s disease, a degenerative disease that affects an es-timated 4.5 million people in the U.S., can be treated far more effectively when it is detected early. There are numerous chal-lenges to early detection. One is objectivity, since caretakers are often emotionally invested in the health of the patients, who may be their family members. Consistency of administration can also be an issue, especially where longitudinal results from different examiners are compared. Finally, the frequency of testing can be adversely affected by scheduling or cost con-straints for in-home psychometrician visits. The kiosk system described in this paper, currently deployed in homes around the country, uses speech technology to provide advantages that ad-dress these challenges. Index Terms: spoken language systems, in-home psychometric testing, Alzheimer’s disease.

“Clear ” (CLR) speech is a speaking style that speakers adopt to be understood correctly in a difficult communication environment. Studies have shown that CLR speech, as opposed to “conversational” (CNV) speech, has significantly higher intelligibility in various conditions. While many differences in acoustic features have been identified, it is not known which individual feature or combinations of features cause the higher intelligibility of CLR speech. The objectives of the current study are to examine whether it is possible to improve speech intelligibility by approximating CLR speech features and to determine which acoustic features contribute to intelligibility. Our approach creates speech samples that combine acoustic features of CNV and CLR speech, using a hybridization algorithm. Results with normalhearing listeners showed significant sentence-level intelligibility improvements of 11–23 % over CNV speech when replacing certain acoustic features with those from CLR speech.

While there are numerous methods for estimating the fundamental frequency (F0) of speech, existing methods often suffer from pitch doubling or halving errors. Heuristics can be added to constrain the range of allowable F0 values, but it is still difficult to appropriately set the algorithm parameters if one does not know in advance the speaker’s age or gender. The proposed method is distinct from most other F0estimation algorithms in that it does not use autocorrelation, cepstral, or pattern-recognition techniques. Instead, information from 32 band-pass filters is combined at every frame, a Viterbi search provides an initial F0-contour estimate, and this estimate is then refined based on intensity discrimination of the speech signal. Despite the use of a large number of filters (which provide complementary information and hence robustness), the implementation works in less than real-time on a 2.4 GHz processor without optimization for processing speed. Results are presented for two corpora, one corpus of an adult male and one of children of different ages. For the first corpus, average absolute error is 4.10 Hz (percent error of 4.15%); for the second corpus, average absolute error is 7.74 Hz (percent error of 3.38%). 1.

This paper presents an early study on building Vietnamese large vocabulary continuous speech recognition with concentration on choosing type of units and feature set. Our experiments were done using the HTK Toolkit and VOV broadcast corpus. The results show that the recognizer with mixture units achieved better performance than recognizers with initial-final units or phoneme units. Among feature sets applied to the mixture unit recognizer, MFCC has performance somewhat better than PLP, and the combination of MFCC and F0 features increases the accuracy of the Vietnamese recognition system. 1.

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.

This paper presents a method for speaker-independent automatic phonetic alignment that is distinguished from standard HMM-based “forced alignment ” in three respects: (1) specific acoustic-phonetic features are used, in addition to PLP features, by the phonetic classifier; (2) the units of classification consist of distinctive phonetic features instead of phonemes; and (3) observation probabilities depend not only on the current state, but also on the state transition information. This proposed method is compared with a state-of-the-art baseline forcedalignment system on a number of corpora, including telephone speech, microphone speech, and children’s speech. The new method has agreement of 92.57 % within 20 msec on the TIMIT corpus, which is a 26 % reduction in error over the baseline method (with 89.95 % agreement on TIMIT). Average reduction in error over all corpora is 28%. 1.

The development of a speaker independent “general purpose” phonetic recognizer for Italian is described. The CSLU Toolkit was used to develop and implement the system. The recognizer, based on a frame-based hybrid HMM/ANN architecture trained on context-dependent categories to account for coarticulatory variation, recognizes 38 different phonemes (not including silence or closures), and can distinguish between stressed and unstressed vowels as well as open and closed vowels. The APASCI corpus, containing nearly 2500 sentences read by 100 speakers, where the sentences have been designed to maximize the number of phonemes occurring in different contexts, was used for training and testing. As of the time of this writing, a phoneme-level accuracy of 82.90% on the development set and of 80.53% on the test set has been obtained. This level of accuracy is much greater than on a similar English-language corpus (with state-of-the-art performance of slightly better than 70%) and it represents the best performance obtained so far on this corpus.

The development of a speaker independent connected “digits ” recognizer for Italian is described. The CSLU Speech Toolkit was used to develop and implement the system which is based on an hybrid ANN/HMM architecture. The recognizer is trained on contextdependent categories to account for coarticulatory variation. Various front-end processing was compared and, when the best features (MFCC with CMS + ∆) were considered, there was a 98.68 % word recognition accuracy (90.76 % sentence recognition accuracy) on a test set of the FIELD continuous digits recognition task. 1.

Detection of burst-related impulses, such as those accompanying plosive stop consonants, is an important problem for accurate measurement of acoustic features for recogntion (e.g., voice-onset-time) and for accurate automatic phonetic alignment. The proposed method of burst detection utilizes techniques for identifying and combining information about specific acoustic characteristics of bursts. One key element of the proposed method is the use of a measurement of intensity discrimination based on models from perceptual studies. Our experiments compared the proposed method of burst detection to the support vector machine (SVM) method, described below. The total error rate for the proposed method is 13.2 % on the test-set partition of the TIMIT corpus, compared to a total error rate of 24 % for the SVM method. 1.

Text-to-speech synthesis research has moved away from building general purpose systems based on an understanding of human language and speech production towards building systems based on statistical algorithms applied to large text and speech corpora, and, recently, towards building such systems for specific domains. Despite substantial progress, the overall quality of even the best systems is often still inadequate for broad user acceptance in applications that cannot also be handled with simple phrase splicing. This tutorial paper analyzes which problems must be addressed to achieve the goal of generating naturalsounding speech in limited domains in a cost-effective way, and the roles of data and rules as we work towards solutions. 1.

We describe a corpus of children’s speech, called the OGI Kids’ Speech corpus, and a speaker- and vocabulary-independent recognition system trained and evaluated with these data. The corpus is composed of both prompted and spontaneous speech from 1100 children from kindergarten through grade 10. The prompted speech was presented as text appearing below an animated character (Baldi) that produced accurate visible speech synchronized with recorded prompts. The speech and text consists of isolated words, sentences, and digit strings. A phonetic recognizer was trained using an HMM/ANN framework, with training data taken from intervals of speech associated with phonetic segments in the isolated words in the corpus. Phonetic segments were derived using automatic phonetic alignment. To find out how well the recognizer is able to generalize to new words not found in the training set, we performed two test-set evaluations: one using a new set of utterances from the set of 205 words spoken in isolation (similar to the data used to train the recognizer) and one using words from the prompted sentences. Results were dramatically different (97.5 % for isolated vs. 37.9 % for words in sentences), and we explore methods that may be used to improve the recognizer’s ability to generalize to new words. 1.

This paper describes a set of experiments on neural-network training and search techniques that, when combined, have resulted in a 54% reduction in error on the continuous digits recognition task. The best system had word-level accuracy of 97.52% on a test set of the OGI 30K Numbers corpus, which contains naturally-produced continuous digit strings recorded over telephone channels. Experiments investigated effects of the feature set, the amount of data used for training, the type of context-dependent categories to be recognized, the values for duration limits, and the type of grammar. The experiments indicate that the grammar and duration limits had a greater effect on recognition accuracy than the output categories, cepstral features, or a 50% increase in the amount of training data. 1. INTRODUCTION The recognizers in the CSLU Toolkit use a hybrid HMM/ANN framework [1]. In these systems, frame-based recognition is done with context-dependent sub-phonetic states, where the state prob...

A set of freely available, universal speech tools is needed to accelerate progress in the speech technology. The CSLU Toolkit represents an effort to make the core technology and fundamental infrastructure accessible, affordable and easy to use. The CSLU Toolkit has been under development for five years. This paper describes recent improvements, additions and uses of the CSLU Toolkit. 1. INTRODUCTION Since 1993, the Center for Spoken Language Understanding (CSLU) has focused on incorporating state-of-the-art spokenlanguage technology into a portable, comprehensive and easyto -use software environment. The result of these efforts is the CSLU Toolkit. The toolkit integrates learning materials, authoring tools and core technologies such as speech recognition, text-to-speech synthesis, facial animation and speech reading. The toolkit is designed to support basic research, development and education activities related to spoken language systems and human-computer interfaces. What are our ...

In exploring new ways of looking at speech data, we have developed an alternative method of segmentation for training a neural-network-based digit-recognition system. Whereas previous methods segment the data into monophones, biphones, or triphones and train on each sub-phone unit in several broad-category contexts, our new method uses modified diphones to train on the regions of greatest spectral change as well as the regions of greatest stability. Although we account for regions of spectral stability, we do not require their presence in our word models. Empirical evidence for the advantage of this new method is seen by the 13% reduction in word-level error that was achieved on a test set of the OGI Numbers corpus. Comparison was made to a baseline system that used context-independent monophones and context-dependent biphones and triphones.