Abstract: System, method and media for quantifying bulbar function of a subject. At a high level, embodiments of the invention measure and quantify bulbar function of a test subject based on video data, audio data, or other sensor data of a subject performing a test of bulbar function, such as speech, swallowing, and orofacial movements. This sensor data is then analyzed to identify key events such as syllable enunciations. Based on one or more characteristics of these key events (such as, for example, their rate, count, assessed accuracy, or trends over time), the bulbar function of the subject can accurately, reliably, and objectively be quantified.

Abstract: System, method and media for quantifying bulbar function of a subject. At a high level, embodiments of the invention measure and quantify bulbar function of a test subject based on video data, audio data, or other sensor data of a subject performing a test of bulbar function, such as speech, swallowing, and orofacial movements. This sensor data is then analyzed to identify key events such as syllable enunciations. Based on one or more characteristics of these key events (such as, for example, their rate, count, assessed accuracy, or trends over time), the bulbar function of the subject can accurately, reliably, and objectively be quantified.

Abstract: A speaker adaptation technique based on the separation of speech spectra variation sources is developed for improving speaker-independent continuous speech recognition. The variation sources include speaker acoustic characteristics, and contextual dependency of allophones. Statistical methods are formulated to normalize speech spectra based on speaker acoustic characteristics and then adapt mixture Gaussian density phone models based on speaker phonologic characteristics. Adaptation experiments using short calibration speech (5 sec./speaker) have shown substantial performance improvement over the baseline recognition system.

Abstract: A speaker normalization method is described based on spectral shifts in the auditory filter domain. The method is characterized by using an estimated vocal tract length as a criterion to determine the spectral shift value. Certain constraints are found to be necessary for the shift in the auditory filter domain, and two techniques based on these constraints, the One-Bark shift and the refined Bark-scale shift, are introduced. When tested in vowel classification experiments, significant performance improvement was obtained for both techniques. The method is useful for speaker normalization in speaker-independent speech recognition.

Abstract: A self-learning speaker adaptation method for automatic speech recognition is provided. The method includes building a plurality of Gaussian mixture density phone models for use in recognizing speech. The Gaussian mixture density phone models are used to recognize a first utterance of speech from a given speaker. After the first utterance of speech has been recognized, the recognized first utterance of speech is used to adapt the Gaussian mixture density hone models for use in recognizing a subsequent utterance of speech from that same speaker, whereby the Gaussian mixture density phone models are automatically adapted to that speaker in self-learning fashion to thereby produce a plurality of adapted Gaussian mixture density phone models.

Abstract: A model-training module generates mixture Gaussian density models from speech training data for continuous, or isolated word speech recognition systems. Speech feature sequences are labeled into segments of states of speech units using Viterbi-decoding based optimized segmentation algorithm. Each segment is modeled by a Gaussian density, and the parameters are estimated by sample mean and sample covariance. A mixture Gaussian density is generated for each state of each speech unit by merging the Gaussian densities of all the segments with the same corresponding label. The resulting number of mixture components is proportional to the dispersion and sample size of the training data. A single, fully merged, Gaussian density is also generated for each state of each speech unit. The covariance matrices of the mixture components are selectively smoothed by a measure of relative sharpness of the Gaussian density and the smoothing can also be done blockwise.

Abstract: A word hypothesis module for speech decoding consists of four submodules: vowel center detection, bidirectional tree searches around each vowel center, forward-backward pruning, and additional short words hypotheses. By detecting the strong energy vowel centers, a vowel-centered lexicon tree can be placed at each vowel center and searches can be performed in both the left and right directions, where only simple phone models are used for fast acoustic match. A stage-wise forward-backward technique computes the word-beginning and word-ending likelihood scores over the generated half-word lattice for further pruning of the lattice. To avoid potential miss of short words with weak energy vowel centers, a lexicon tree is compiled for these words and tree searches are performed between each pair of adjacent vowel centers. The integration of the word hypothesizer with a top-down Viterbi beam search in continuous speech decoding provides two-pass decoding which significantly reduces computation time.

Abstract: A model-training module generates mixture Gaussian density models from speech training data for continuous, or isolated word HMM-based speech recognition systems. Speech feature sequences are labeled into segments of states of speech units using Viterbi-decoding based optimized segmentation algorithm. Each segment is modeled by a Gaussian density, and the parameters are estimated by sample mean and sample covariance. A mixture Gaussian density is generated for each state of each speech unit by merging the Gaussian densities of all the segments with the same corresponding label. The resulting number of mixture components is proportional to the dispension and sample size of the training data. A single, fully merged, Gaussian density is also generated for each state of each speech unit. The covariance matrices of the mixture components are selectively smoothed by a measure of relative sharpness of the Gaussian density.