Abstract: A system and method for learning alternate pronunciations for speech recognition is disclosed. Alternative name pronunciations may be covered, through pronunciation learning, that have not been previously covered in a general pronunciation dictionary. In an embodiment, the detection of phone-level and syllable-level mispronunciations in words and sentences may be based on acoustic models trained by Hidden Markov Models. Mispronunciations may be detected by comparing the likelihood of the potential state of the targeting pronunciation unit with a pre-determined threshold through a series of tests. It is also within the scope of an embodiment to detect accents.

Abstract: A system and method for learning alternate pronunciations for speech recognition is disclosed. Alternative name pronunciations may be covered, through pronunciation learning, that have not been previously covered in a general pronunciation dictionary. In an embodiment, the detection of phone-level and syllable-level mispronunciations in words and sentences may be based on acoustic models trained by Hidden Markov Models. Mispronunciations may be detected by comparing the likelihood of the potential state of the targeting pronunciation unit with a pre-determined threshold through a series of tests. It is also within the scope of an embodiment to detect accents.

Abstract: A system and method for learning alternate pronunciations for speech recognition is disclosed. Alternative name pronunciations may be covered, through pronunciation learning, that have not been previously covered in a general pronunciation dictionary. In an embodiment, the detection of phone-level and syllable-level mispronunciations in words and sentences may be based on acoustic models trained by Hidden Markov Models. Mispronunciations may be detected by comparing the likelihood of the potential state of the targeting pronunciation unit with a pre-determined threshold through a series of tests. It is also within the scope of an embodiment to detect accents.

Abstract: Methods and arrangements for accommodating and synthesizing data for seeking broad-based solutions to complex problems. Logical models with respect to infrastructure in a first location are created. System-level models are created with respect to the logical models, and simulations are performed on the system-level models to verify expected behavior. Feedback is employed from the performed simulations to forecast changes in the system-level models.

Abstract: Methods and arrangements for employing roadside acoustics sensing in ascertaining traffic density states. Traffic monitoring input is received from a road segment, the traffic monitoring input including traffic audio input. The traffic monitoring input is processed and the processed traffic monitoring input is classified with a predetermined traffic density state. The classified traffic monitoring input is combined with other classified traffic monitoring input.

Abstract: A system and method are presented for selectively biased linear discriminant analysis in automatic speech recognition systems. Linear Discriminant Analysis (LDA) may be used to improve the discrimination between the hidden Markov model (HMM) tied-states in the acoustic feature space. The between-class and within-class covariance matrices may be biased based on the observed recognition errors of the tied-states, such as shared HMM states of the context dependent tri-phone acoustic model. The recognition errors may be obtained from a trained maximum-likelihood acoustic model utilizing the tied-states which may then be used as classes in the analysis.

Abstract: A system and method are presented for acoustic data selection of a particular quality for training the parameters of an acoustic model, such as a Hidden Markov Model and Gaussian Mixture Model, for example, in automatic speech recognition systems in the speech analytics field. A raw acoustic model may be trained using a given speech corpus and maximum likelihood criteria. A series of operations are performed, such as a forced Viterbi-alignment, calculations of likelihood scores, and phoneme recognition, for example, to form a subset corpus of training data. During the process, audio files of a quality that does not meet a criterion, such as poor quality audio files, may be automatically rejected from the corpus. The subset may then be used to train a new acoustic model.

Abstract: Methods and arrangements for accommodating and synthesizing data for seeking broad-based solutions to complex problems. Logical models with respect to infrastructure in a first location are created. System-level models are created with respect to the logical models, and simulations are performed on the system-level models to verify expected behavior. Feedback is employed from the performed simulations to forecast changes in the system-level models.

Abstract: Methods and arrangements for employing roadside acoustics sensing in ascertaining traffic density states. Traffic monitoring input is received from a road segment, the traffic monitoring input including traffic audio input. The traffic monitoring input is processed and the processed traffic monitoring input is classified with a predetermined traffic density state. The classified traffic monitoring input is combined with other classified traffic monitoring input.

Abstract: Techniques for protecting information in an audio file are provided. The techniques include obtaining an audio file, detecting information beating one or more segments in a speech signal, wherein the information comprises information sought for protection, encrypting the information sought for protection by scrambling the one or more segments using a scrambling filter, and selectively decrypting an amount of the encrypted information, wherein the amount of the encrypted information to be decrypted depends on user access privilege, and wherein selectively decrypting the amount of the encrypted information protects said amount of the encrypted information. Techniques are also provided for protecting information in an audio file.

Abstract: Techniques for protecting information in an audio file are provided. The techniques include obtaining an audio file, detecting information bearing one or more segments in a speech signal, wherein the information comprises information sought for protection, encrypting the information sought for protection by scrambling the one or more segments using a scrambling filter, and selectively decrypting an amount of the encrypted information, wherein the amount of the encrypted information to be decrypted depends on user access privilege, and wherein selectively decrypting the amount of the encrypted information protects said amount of the encrypted information. Techniques are also provided for protecting information in an audio file.

Abstract: Techniques for protecting information in an audio file are provided. The techniques include obtaining an audio file, detecting information bearing one or more segments in a speech signal, wherein the information comprises information sought for protection, encrypting the information sought for protection by scrambling the one or more segments using a scrambling filter, and selectively decrypting an amount of the encrypted information, wherein the amount of the encrypted information to be decrypted depends on user access privilege, and wherein selectively decrypting the amount of the encrypted information protects said amount of the encrypted information. Techniques are also provided for protecting information in an audio file.

Abstract: Techniques for protecting information in an audio file are provided. The techniques include obtaining an audio file, detecting information beating one or more segments in a speech signal, wherein the information comprises information sought for protection, encrypting the information sought for protection by scrambling the one or more segments using a scrambling filter, and selectively decrypting an amount of the encrypted information, wherein the amount of the encrypted information to be decrypted depends on user access privilege, and wherein selectively decrypting the amount of the encrypted information protects said amount of the encrypted information. Techniques are also provided for protecting information in an audio file.