Using cross-decoder phone coocurrences in phonotactic language recognition

Phonotactic language recognizers are based on the ability of phone decoders to produce phone sequences containing acoustic, phonetic and phonological information, which is partially dependent on the language. Input utterances are de-coded and then scored by means of models for the target lan-guages. Commonly, various decoders are applied in parallel and fused at the score level. A kind of complementarity ef-fect is expected when fusing scores, since each decoder is assumed to extract different (and complementary) informa-tion from the input utterance. This assumption is supported by the performance improvements attained when fusing sys-tems. However, decodings are processed in a fully uncou-pled way, their time alignment (and the information that may be extracted from it) being completely lost. In this paper, a simple approach is proposed, which takes into account time alignment information, by considering cross-decoder phone coocurrences at the frame level. To evaluate the approach, a choice of open software (BUT front-end and phone decoders, SRI-LM toolkit, libSVM, FoCal) is used, and experiments are carried out on the NIST LRE2007 database. Adding phone coocurrences to the baseline phonotactic systems pro-vides slight performance improvements, revealing the poten-tial benefit of using cross-decoder dependencies for language modeling

A comparison of features for large population speaker identification

Bibliography: leaves 95-104.Speech recognition systems all have one criterion in common; they perform better in a controlled environment using clean speech. Though performance can be excellent, even exceeding human capabilities for clean speech, systems fail when presented with speech data from more realistic environments such as telephone channels. The differences using a recognizer in clean and noisy environments are extreme, and this causes one of the major obstacles in producing commercial recognition systems to be used in normal environments. It is the lack of performance of speaker recognition systems with telephone channels that this work addresses. The human auditory system is a speech recognizer with excellent performance, especially in noisy environments. Since humans perform well at ignoring noise more than any machine, auditory-based methods are the promising approaches since they attempt to model the working of the human auditory system. These methods have been shown to outperform more conventional signal processing schemes for speech recognition, speech coding, word-recognition and phone classification tasks. Since speaker identification has received lot of attention in speech processing because of its waiting real-world applications, it is attractive to evaluate the performance using auditory models as features. Firstly, this study rums at improving the results for speaker identification. The improvements were made through the use of parameterized feature-sets together with the application of cepstral mean removal for channel equalization. The study is further extended to compare an auditory-based model, the Ensemble Interval Histogram, with mel-scale features, which was shown to perform almost error-free in clean speech. The previous studies of Elli to be more robust to noise were conducted on speaker dependent, small population, isolated words and now are extended to speaker independent, larger population, continuous speech. This study investigates whether the Elli representation is more resistant to telephone noise than mel-cepstrum as was shown in the previous studies, when now for the first time, it is applied for speaker identification task using the state-of-the-art Gaussian mixture model system

LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech

Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related tasks are now being approached with pre-trained models. This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community, and an evaluation protocol made of six downstream tasks to complement existing benchmarks. LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for speech with the investigation of frozen versus fine-tuned downstream models, task-agnostic versus task-specific pre-trained models as well as a discussion on the carbon footprint of large-scale model training.Comment: Under submission at Computer Science and Language. Preprint allowe

A Dynamic Vocabulary Speech Recognizer Using Real-Time, Associative-Based Learning

Conventional speech recognizers employ a training phase during which many of their parameters are configured - including vocabulary selection, feature selection, and decision mechanism tailoring to these selections. After this stage during normal operation, these traditional recognizers do not significantly alter any of these parameters. Conversely this work draws heavily on high level human thought patterns and speech perception to outline a set of precepts to eliminate this training phase and instead opt to perform all its tasks during the normal operation. A feature space model is discussed to establish a set of necessary and sufficient conditions to guide real-time feature selection. Detailed implementation and preliminary results are also discussed. These results indicate that benefits of this approach can be seen in increased speech recognizer adaptability while still retaining competitive recognition rates in controlled environments. Thus this can accommodate such changes as varying vocabularies, class migration, and new speakers

Apraxia World: Deploying a Mobile Game and Automatic Speech Recognition for Independent Child Speech Therapy

Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. The therapy game, called Apraxia World, delivers customizable, repetition-based speech therapy while children play through platformer-style levels using typical on-screen tablet controls; children complete in-game speech exercises to collect assets required to progress through the levels. Additionally, Apraxia World provides pronunciation feedback according to an automated pronunciation evaluation system running locally on the tablet. Apraxia World offers two advantages over current commercial and research speech therapy games; first, the game provides extended gameplay to support long therapy treatments; second, it affords some therapy practice independence via automatic pronunciation evaluation, allowing caregivers to lightly supervise instead of directly administer the practice. Pilot testing indicated that children enjoyed the game-based therapy much more than traditional practice and that the exercises did not interfere with gameplay. During a longitudinal study, children made clinically-significant pronunciation improvements while playing Apraxia World at home. Furthermore, children remained engaged in the game-based therapy over the two-month testing period and some even wanted to continue playing post-study. The second part of the dissertation explores word- and phoneme-level pronunciation verification for child speech therapy applications. Word-level pronunciation verification is accomplished using a child-specific template-matching framework, where an utterance is compared against correctly and incorrectly pronounced examples of the word. This framework identified mispronounced words better than both a standard automated baseline and co-located caregivers. Phoneme-level mispronunciation detection is investigated using a technique from the second-language learning literature: training phoneme-specific classifiers with phonetic posterior features. This method also outperformed the standard baseline, but more significantly, identified mispronunciations better than student clinicians

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Unsupervised pattern discovery in speech : applications to word acquisition and speaker segmentation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2007.Includes bibliographical references (p. 167-176).We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a pre-specified inventory of lexical units (i.e. phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multi-word phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.(cont.) We demonstrate two applications of our pattern discovery procedure. First, we propose and evaluate two methods for automatically identifying sound clusters generated through pattern discovery. Our results show that high identification accuracy can be achieved for single word clusters using a constrained isolated word recognizer. Second, we apply acoustic pattern matching to the problem of speaker segmentation by attempting to find word-level speech patterns that are repeated by the same speaker. When used to segment a ten hour corpus of multi-speaker lectures, we found that our approach is able to generate segmentations that correlate well to independently generated human segmentations.by Alex Seungryong Park.Ph.D