A TMS320C54 system for effective online Signature Verification using Hidden Markov Models

In this paper we present a scheme for real time implementation of a Hidden Markov Model based Signature Verification System on a TMS320C54 processor. Here we explain in detail our overall methodology and the subsequent DSP implementation. We also propose two new algorithms which would further facilitate real-time operation. We use the Baum-We1ch Algorithm for training the HMM and the Viterbi Algorithm for the testing of our proposed system. It may be noted that the technique of HMMs have hitherto been applied for speech modelling and only recently has its application to the field of Signature Verification been considered. Our proposed system has an overall accuracy of 11.64% FAR and 0.64% FRR

Multilingual Language Processing From Bytes

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning "from scratch" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Abstract In this paper, we tackle the problem of speech enhancement from two fronts: speech modeling and multisensory input. We present a new speech model based on statistics of magnitude-normalized complex spectra of speech signals. By performing magnitude normalization, we are able to get rid of huge intra-and inter-speaker variation in speech energy and to build a better speech model with a smaller number of Gaussian components. To deal with real-world problems with multiple noise sources, we propose to use multiple heterogeneous sensors, and in particular, we have developed microphone headsets that combine a conventional air microphone and a bone sensor. The bone sensor makes direct contact with the speaker's temple (area behind the ear), and captures the vibrations of the bones and skin during the process of vocalization. The signals captured by the bone microphone, though distorted, contain useful audio information, especially in the low frequency range, and more importantly, they are very robust to external noise sources (stationary or not). By fusing the bone channel signals with the air microphone signals, much improved speech signals have been obtained

Frame-semantic parsing

Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target's locally expressed semantic arguments. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, resulting in qualitatively better structures than naïve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software.United States. Defense Advanced Research Projects Agency (DARPA grant NBCH-1080004)National Science Foundation (U.S.) (NSF grant IIS-0836431)National Science Foundation (U.S.) (NSF grant IIS-0915187)Qatar National Research Fund (NPRP 08-485-1-083

MULTI-SENSORY SPEECH PROCESSING: INCORPORATING AUTOMATICALLY EXTRACTED HIDDEN DYNAMIC INFORMATION

We describe a novel technique for multi-sensory speech processing for enhancing noisy speech and for improved noiserobust speech recognition. Both air- and bone-conductive microphones are used to capture speech data where the bone sensor contains virtually noise-free hidden dynamic information of clean speech in the form of formant trajectories. The distortion in the bone-sensor signal such as teethclacking and noise leakage can be effectively removed by making use of the automatically extracted formant information from the bone-sensor signal. This paper reports an improved technique for synthesizing speech waveforms based on the LPC cepstra computed analytically from the formant trajectories. When this new signal stream is fused with the other available speech data streams, we achieved improved performance for noisy speech recognition. 1

Virtual evidence for training speech recognizers using partially labeled data

Collecting supervised training data for automatic speech recognition (ASR) systems is both time consuming and expensive. In this paper we use the notion of virtual evidence in a graphical-model based system to reduce the amount of supervisory training data required for sequence learning tasks. We apply this approach to a TIMIT phone recognition system, and show that our VE-based training scheme can, relative to a baseline trained with the full segmentation, yield similar results with only 15.3 % of the frames labeled (keeping the number of utterances fixed).