Large language models (LLMs) are increasingly used for zero-shot conversation summarization, but often exhibit positional bias—tending to overemphasize content from the beginning or end of a conversation while neglecting the middle. To address this issue, we introduce PoSum-Bench, a comprehensive benchmark for evaluating positional bias in conversational summarization, featuring diverse English and French conversational datasets spanning formal meetings, casual conversations, and customer service interactions. We propose a novel semantic similarity-based sentence-level metric to quantify the direction and magnitude of positional bias in model-generated summaries, enabling systematic and reference-free evaluation across conversation positions, languages, and conversational contexts.Our benchmark and methodology thus provide the first systematic, cross-lingual framework for reference-free evaluation of positional bias in conversational summarization, laying the groundwork for developing more balanced and unbiased summarization models.

This article proposes a new confidence measure estimated for concept hypotheses provided by a semantic language model used in the context of a dialog application. This confidence measure is based upon the ontology and more precisely, upon the semantic relations between concepts. It aims at measuring how high a concept hypothesis is related to the other hypotheses of an utterance. The semantic relation confidence measure is evaluated alone, and in combination with a classical acoustic confidence measure. The two measures are also used as parameters of a decision tree. It is shown that the two confidence measures are complementary and yield good performance in terms of cross entropy relative reduction .

This paper describes the construction and use of language models specific to semantic concepts. A tolerance mechanism is introduced in order to design conceptual constituents that cover longer word patterns while remaining robust to spontaneous speech disfluencies and recognition errors. These models are obtained from sentence patterns which grow from essential information to longer sequences of words. It is shown that these augmented conceptual constituents provide reductions in concept error rates on a spoken dialog application task. The composition of semantic constituents into semantic structures is discussed together with the introduction of language models for semantic composition. Experiments are described which show the advantage of using these language models to take into account structured semantic information.

In this paper, the feasibility of designing a speech-recognition based telephony server for in-car applications with an acceptable recognition rate is investigated. The whole acoustic channel (sound pickup, sound transmission over the cellular network, feature extraction) is evaluated: the loss or the gain in performance due to each element is quantified. More precisely, two sound pickup systems (a hypercardioid microphone and a microphone array) were tested. A standard MFCC and the Aurora advanced front-ends were evaluated. Recognition performance was measured before and after transmission over a cellular (GSM) network. The gain of using either a robust sound recording device or noise robust front-end is demonstrated.

This paper deals with the recognition of French city-names over the telephone. This recognition task, critical in many applications, involves a 40,000 city-name vocabulary, ranging from short monosyllabic words to long official compound-names. Data collected from a field experiment are analyzed, and several ways of improving speech recognition performance are investigated. This includes a careful checking of the pronunciation lexicon, acceptation of shorter forms (common names), adaptation of the acoustic models and introduction of specific noise models as well as a few frequent words and expressions to facilitate out-of-vocabulary data rejection. Experiments show that all these techniques help improving the overall recognition performances and nicely combine together.

An acoustic mismatch between a given utterance and a model degrades the performance of the speech recognition process. We choose to model speech by hidden Markov models (HMMs) in the cepstrum domain and the mismatch by a parametric function. In order to reduce the mismatch, one has to estimate the parameters of this function. We present a frame synchronous estimation of these parameters. We show that the parameters can be computed recursively. Thanks to such methods, parameters variations can be tracked. We give general equations and study the particular case of an affine transform. Finally, we report recognition experiments carried out over both PSTN and cellular telephone network to show the efficiency of the method in a real context.

We propose using Hidden Markov Models (HMMs) associated with the cepstrum coefficients as a speech signal model in order to perform equalization or noise removal. The MUlti-path Stochastic Equalization (MUSE) framework allows one to process data at the frame level: it is an on-line adaptation of the model. More precisely, we apply this technique to perform bias removal in the cepstral domain in order to increase the robustness of automatic speech recognizers. Recognition experiments on two databases recorded on both PSN and GSM networks show the efficiency of the proposed method.