Xavier L. Aubert

An overview of decoding techniques for large vocabulary continuous speech recognition

Abstract A number of decoding strategies for large vocabulary continuous speech recognition (LVCSR) are examined from the viewpoint of their search space representation. Different design solutions are compared with respect to the integration of linguistic and acoustic constraints, as implied by m -gram language models (LM) and cross-word (CW) phonetic contexts. This study is structured along two main axes: the network expansion and the search algorithm itself. The network can be expanded statically or dynamically while the search can proceed either time-synchronously or asynchronously which leads to distinct architectures. Three broad classes of decoding methods are briefly reviewed: the use of weighted finite state transducers (WFST) for static network expansion, the time-synchronous dynamic-expansion search and the asynchronous stack decoding. Heuristic methods for further reducing the search space are also considered. The main approaches are compared and some prospective views are formulated regarding possible future avenues.

Cuffless Estimation of Systolic Blood Pressure for Short Effort Bicycle Tests: The Prominent Role of the Pre-Ejection Period

This paper investigates the specific contributions of the pre-ejection period (PEP) and pulse transit time (PTT) for blood pressure estimation based on the pulse wave methodology. We show that in short-term physical stress tests, PEP dominates PTT variations raising the question of a suitable blood pressure calibration. A model using a generalized pulse wave velocity achieves acceptable accuracy for systolic blood pressure estimation, given our experimental conditions

Multilingual large vocabulary speech recognition: the European SQALE project

Abstract This paper describes the S qale project in which the ARPA large vocabulary evaluation paradigm was adapted to meet the needs of European multilingual speech recognition development. It involved establishing a framework for sharing training and test materials, defining common protocols for training and testing systems, developing systems, running an evaluation and analysing the results. The specifically multilingual issues addressed included the impact of the language on corpora and test set design, transcription issues, evaluation metrics, recognition system design, cross-system and cross-language performance, and results analysis. The project started in December 1993 and finished in September 1995. The paper describes the evaluation framework and the results obtained. The overall conclusions of the project were that the same general approach to recognition system design is applicable to all the languages studied although there were some language specific problems to solve. It was found that the evaluation paradigm used within ARPA could be used within the European context with little difficulty and the consequent sharing amongst the sites of training and test materials and language-specific expertise was highly beneficial.

Continuous mixture densities and linear discriminant analysis for improved context-dependent acoustic models

Linear discriminant analysis (LDA) experiments reported previously (ICASSP-92 vol.1, p.13-16), are extended to context-dependent models and speaker-independent large vocabulary continuous speech recognition. Two variants of using mixture densities are compared: state-specific modeling and the monophone-tying approach where densities are shared across the states relevant to the same phoneme. Results are presented on the DARPA Resource Management (RM) task for both speaker-dependent (SD) and speaker-independent (SI) parts. Using triphone models based on LDA and continuous mixture densities, significant improvements have been observed and the following word error rates have been achieved: for the SD part, 7.8% without grammar and 1.5% with word pair; and for the SI part, 17.2% and 4.6%, respectively. These scores are averaged over 1200 SD or SI evaluation sentences and are among the best published so far on the RM database.<<ETX>>

Large vocabulary continuous speech recognition of Broadcast News - The Philips/RWTH approach

Abstract Automatic speech recognition of real-live broadcast news (BN) data (Hub-4) has become a challenging research topic in recent years. This paper summarizes our key efforts to build a large vocabulary continuous speech recognition system for the heterogenous BN task without inducing undesired complexity and computational resources. These key efforts included: • automatic segmentation of the audio signal into speech utterances; • efficient one-pass trigram decoding using look-ahead techniques; • optimal log-linear interpolation of a variety of acoustic and language models using discriminative model combination (DMC); • handling short-range and weak longer-range correlations in natural speech and language by the use of phrases and of distance-language models; • improving the acoustic modeling by a robust feature extraction, channel normalization, adaptation techniques as well as automatic script selection and verification. The starting point of the system development was the Philips 64k-NAB word-internal triphone trigram system. On the speaker-independent but microphone-dependent NAB-task (transcription of read newspaper texts) we obtained a word error rate of about 10%. Now, at the conclusion of the system development, we have arrived at Philips at an DMC-interpolated phrase-based crossword-pentaphone 4-gram system. This system transcribes BN data with an overall word error rate of about 17%.

Comparison of systolic time interval measurement modalities for portable devices

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population

The Pulse Transit Time (PTT) is generally assumed to be a good surrogate measure to comfortably track blood pressure (BP) and blood pressure changes. This paper investigates PTT variations for healthy young subjects during a sequence of short-term physical exercises. PTT was measured by two different methodologies having different measurement accuracies as well as underlying assumptions: the total PTT from heart to fingertip and the difference of fingertip and earlobe PTTs. Small non consistent changes and very low correlation of both PTTs with systolic blood pressure (SBP) have been observed for the study population (−0.19 ± 0.45 and 0.22 ± 0.46). In conclusion, there might be a need for an improved measurement accuracy of the sensors and data processing techniques in use. The applicability of the Moens-Korteweg equation is also questionable for young people having flexible arteries. In this case, significant radius changes do occur in the large arteries during exercise, which might counteract a PTT decrease with the BP elevation. These radius effects are excluded from the Moens-Korteweg model.

Ambulatory estimation of human circadian phase using models of varying complexity based on non-invasive signal modalities

In this work, we introduce a number of models for human circadian phase estimation in ambulatory conditions using various sensor modalities. Machine learning techniques have been applied to ambulatory recordings of wrist actigraphy, light exposure, electrocardiograms (ECG), and distal and proximal skin temperature to develop ARMAX models capturing the main signal dependencies on circadian phase and evaluating them versus melatonin onset times. The most accurate models extracted heart rate variability features from an ECG coupled with wrist activity information to produce phase estimations with prediction errors of ~30 minutes. Replacing the ECG features with skin temperature from the upper leg led to a slight degradation, while less accurate results, in the order of 1 hour, were obtained from wrist activity and light measurements. The trade-off between highest precision and least obtrusive configuration is discussed for applications to sleep and mood disorders caused by a misalignment of the internal phase with the external solar and social times.

The Philips Research system for continuous-speech recognition

This paper gives an overview of the Philips Research system for continuous-speech recognition. The recognition architecture is based on an integrated statistical approach. The system has been successfully applied to various tasks in American English and German, ranging from small vocabulary tasks to very large vocabulary tasks and from recognition only to speech understanding. Here, we concentrate on phoneme-based continuous-speech recognition for large vocabulary recognition as used for dictation, which covers a significant part of our research work on speech recognition. We describe this task and report on experimental results. In order to allow a comparison with the performance of other systems, a section with an evaluation on the standard North American Business news (NAB2) task (dictation of American English newspaper text) is supplied.

Wearable body sensor network towards continuous cuff-less blood pressure monitoring

We present a wearable IEEE 802.15.4-based Body Sensor Network (BSN) that enables continuous cuff-less blood pressure monitoring, opening up new perspectives for hypertension diagnosis and treatment, cardio-vascular event detection, and stress monitoring. Arterial blood pressure is estimated based on the Pulse Arrival Time (PAT), which is measured using a single lead electrocardiogram (ECG) patch on the chest and a photoplethysmogram (PPG) sensor at the finger or ear. Measurement context information-user posture and activity level-is extracted using a 3-D acceleration sensor. Since precise PAT measurements require the synchronization of the BSN devicespsila clocks, the Flooding Time Synchronization Protocol (FTSP) was implemented. The acquired data are stored and displayed on a PDA or a wristwatch. Our BSN can currently operate for up to eight hours and perform PAT measurements under moderate activity conditions. Future work includes higher motion tolerance, posture-corrected blood pressure estimation and on-sensor data processing and storage.