Most current speech recognizers use an observatE9 space based on atS8VV al sequence of measur extn ct from fixed-lengt "frames" (e.g., Mel-cepst-ce Given ahypot9; ical word or sub-word sequence, te acoustO likelihood computp;VW always involves allobservat ion frames,t,;LI t, mapping beting individual frames andintV nal recognizerstr;E will depend on t;hypotEO; zed segmentme;LH There is anotLO tot of recognizer whoseobservat ion space isbetI r represente as anet ork, or graph, where each arc in t; graph correspondst a hypotL;) zed variable-lengt segment tm is represente by a fixed-dimensional "featO e". In suchfeatSE;)E sed recognizers, eachhypotO99 zed segmentme;L will correspondt a segment sequence, orpatH ttHSV tt overall segme ntme aph th; is associato wit a subset of all possible feat revectI s intV tVLI observatEV space. Int;E work we examine a maximum apostW iori decoding stcodin forfeat ure-based recognizers and develop a normalizat ioncrit9S on useful for a segme ntme; ed VitOLO or A # search. Experiment arereport ed for bot phoneto and word recognitco tcog .

This thesis concerns the problem of unknown or out-of-vocabulary (00V) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance.

This paper describes our experiences with developing a realtime telephone-based speech recognizer as part of a conversational system in the weather information domain. This system has been used to collect spontaneous speech data which has proven to be extremely valuable for research in a number of different areas. After describing the corpus we have collected, we describe the development of the recognizer vocabulary, pronunciations, language and acoustic models for this system, the new weighted finite-state transducer–based lexical access component, and report on the current performance of the recognizer under several different conditions. We also analyze recognition latency to verify that the system performs in real time. 1.

The acoustic-phonetic modeling component of most current speech recognition systems calculates a small set of homogeneous frame-based measurements at a single, #xed time-frequency resolution. This thesis presents evidence indicating that recognition performance can be signi#cantly improved through a contrasting approach using more detailed and more diverse acoustic measurements, which we refer to as heterogeneous measurements.

This paper examines the recognition of non-native speech in jupiter, a speaker-independent, spontaneous-speech conversational system. Because the non-native speech in this domain is limited and varied, speaker- and accent-specific methods are impractical. We therefore chose to model all of the non-native data with a single model. In particular, this paper describes an attempt to better model non-native lexical patterns. These patterns are incorporated by applying context-independent phonetic confusion rules, whose probabilities are estimated from training data. Using this approach, the word error rate on a non-native test set is reduced from 20.9% to 18.8%. 1. INTRODUCTION Speech recognition accuracy has been observed to be drastically lower for non-native speakers of the target language than for native speakers [3, 13, 14]. Research on both nonnative accent modeling and dialect-specific modeling shows that large gains in performance can be achieved when the acoustics [1, 9, 14] and ...

This paper describes our experiences with developing a telephone-based speech recognizer as part of a conversational system in the weather information domain. This system has been used to collect spontaneous speech data which has proven to be extremely valuable for research in a number of different areas. After describing the corpus we have collected, we describe the development of the recognizer vocabulary, pronunciations, language and acoustic models for this system, and report on the current performance of the recognizer under several different conditions.

Abstract not found

In spite of decades of research, Automatic Speech Recognition (ASR) is far from reaching the goal of performance close to Human Speech Recognition (HSR). One of the reasons for unsatisfactory performance of the state-of-the-art ASR systems, that are based largely on Hidden Markov Models (HMMs), is the inferior acoustic modeling of low level or phonetic level linguistic information in the speech signal. An acoustic-phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal. But an acoustic phonetic system that carries out large ASR speech recognition tasks, for example, connected word or continuous speech recognition, does not exist. We propose a probabilistic and statistical framework for ASR based on the knowledge of acoustic phonetics for connected word ASR. The proposed system is based on the idea of representation of speech sounds by bundles of binary valued articulatory phonetic features. The probabilistic framework requires only binary classifiers of phonetic features and the knowledge based acoustic correlates of the features for the purpose of connected word speech recognition. We explore the use of Support Vector Machines (SVMs) for binary phonetic feature classification because of the favorable properties well suited to our recognition task that SVMs o#er. In the proposed method, probabilistic segmentation of speech is obtained using SVM based classifiers of manner phonetic features. The linguistically motivated landmarks obtained in each segmentation is used for classification of source and place phonetic features. Probabilistic segmentation paths are constrained using Finite State Automata (FSA) for isolated or connected word recognition. The proposed method could overcome the disadvantages ...

Speech synthesis is an automatic encoding process carried out by machine through which symbols conveying linguistic information are converted into an acoustic waveform. In the past decade or so, a recent trend toward a non-parametric, corpus-based approach has focused on using real human speech as source material for producing novel natural-sounding speech. This work proposes a communication-theoretic formulation in which unit selection is a noisy channel through which an input sequence of symbols passes and an output sequence, possibly corrupted due to the coverage limits of the corpus, emerges. The penalty of approximation is quantified by substitution and concatenation costs which grade what unit contexts are interchangeable and where concatenations are not perceivable. These costs are semi-automatically derived from data and are found to agree with acoustic-phonetic knowledge.

Recently, a computational model of human word recognition, called SpeM, has been developed. In contrast to most current models of human word recognition, SpeM is able to process actual acoustic speech input, and decodes the incoming speech stream into lexical and non-lexical items. This model makes the links between HSR and ASR as explicit as possible. In this paper, we focus on unravelling the structure of the complex search space that is used in SpeM and similar decoding strategies. To that end, it discusses a number of properties of phone lattices in relation to canonical phone representations. Furthermore, we elaborate on the close relation between distances in this search space, and distance measures in search spaces that are based on a combination of acoustic and phonetic features. 1.