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State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor intensive and time-consuming. In this paper, we describe a new method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. We automatically estimate a confidence score for each word of the utterance, exploiting the lattice output of a speech recognizer, which was trained on a small set of transcribed data. We compute utterance confidence scores based on these word confidence scores, then selectively sample the utterances to be transcribed using the utterance confidence scores. In our experiments, we show that we reduce the amount of labeled data needed for a given word accuracy by 27%.

State-of-the-art speech recognition systems are trained using human transcriptions of speech utterances. In this paper, we describe a method to combine active and unsupervised learning for automatic speech recognition (ASR). The goal is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function. For unsupervised learning, we utilize the remaining untranscribed data by using their ASR output and word confidence scores. Our experiments show that the amount of labeled data needed for a given word accuracy can be reduced by 75% by combining active and unsupervised learning.

Lowering the perplexity of a language model does not always translate into higher speech recognition accuracy. Our goal is to improve language models by learning from speech recognition errors. In this paper we present an algorithm that first learns to predict which n–grams are likely to increase recognition errors, and then uses that prediction to improve language models so that the errors are reduced. We show that our algorithm reduces a measure of tracking error by more than 24 % on unseen test data from a Reading Tutor that listens to children read aloud. 1.

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Abstract—This paper proposes two methods to incorporate semantic information into word and concept level confidence measurement. The first method uses tag and extension probabilities obtained from a statistical classer and parser. The second method uses a maximum entropy based semantic structured language model to assign probabilities to each word. Incorporation of semantic features into a lattice posterior probability based confidence measure provides significant improvements compared to posterior probability when used together in an air travel reservation task. At 5% False Alarm (FA) rate relative improvements of 28 % and 61 % in Correct Acceptance (CA) rate are achieved for word level and concept level confidence measurements, respectively. I.

A lot of work has been devoted to the estimation of confidence measures for speech recognizers. In the quite extended case where a word-graph speech recognizer is in use, we will present new confidence measures employing the graph theory that shows us how to estimate some interesting characteristics about the different paths through the graph that constitute the recognition solutions, without the need of expanding them all. We will take advantage of some of these features to generate confidence scores both at the word and sentence level. We will also compare this new confidence scoring to more traditional ones and will find similar behavior with less computational load and with an increase in the simplicity of the approach that will lead to more generalization power of the confidence estimation to different applications of the recognizer. 1.

Within a spoken term detection (STD) system, the decision maker plays an important role in retrieving reliable detections. Most of the state-of-the-art STD systems make decisions based on a confidence measure that is term-independent, which poses a serious problem for out-of-vocabulary (OOV) term detection. In this paper, we study a term-dependent confidence measure based on confidence normalisation and discriminative modelling, particularly focusing on its remarkable effectiveness for detecting OOV terms. Experimental results indicate that the term-dependent confidence provides much more significant improvement for OOV terms than terms in-vocabulary. Index Terms: confidence estimation, spoken term detection, speech recognition

Recognition errors hinder the proliferation of speech recognition (SR) systems. Based on the observation that recognition errors may result in ungrammatical sentences, especially in dictation application where an acceptable level of accuracy of generated documents is indispensable, we propose to incorporate two kinds of linguistic features into error detection: lexical features of words, and syntactic features from a robust lexicalized parser. Transformation-based learning is chosen to predict recognition errors by integrating word confidence scores with linguistic features. The experimental results on a dictation data corpus show that linguistic features alone are not as useful as word confidence scores in detecting errors. However, linguistic features provide complementary information when combined with word confidence scores, which collectively reduce the classification error rate by 12.30 % and improve the F measure by 53.62%. 1

We present a detailed study of confidence estimation for machine translation. Various methods for determining whether MT output is correct are investigated, for both whole sentences and words. Since the notion of correctness is not intuitively clear in this context, different ways of defining it are proposed. We present results on data from the NIST 2003 Chinese-to-English MT evaluation. 1