54,427 research outputs found
Objective and Subjective Evaluation of Wideband Speech Quality
Traditional landline and cellular communications use a bandwidth of 300 - 3400 Hz for transmitting speech. This narrow bandwidth impacts quality, intelligibility and naturalness of transmitted speech. There is an impending change within the telecommunication industry towards using wider bandwidth speech, but the enlarged bandwidth also introduces a few challenges in speech processing. Echo and noise are two challenging issues in wideband telephony, due to increased perceptual sensitivity by users.
Subjective and/or objective measurements of speech quality are important in benchmarking speech processing algorithms and evaluating the effect of parameters like noise, echo, and delay in wideband telephony. Subjective measures include ratings of speech quality by listeners, whereas objective measures compute a metric based on the reference and degraded speech samples. While subjective quality ratings are the gold - standard\u27\u27, they are also time- and resource- consuming. An objective metric that correlates highly with subjective data is attractive, as it can act as a substitute for subjective quality scores in gauging the performance of different algorithms and devices.
This thesis reports results from a series of experiments on subjective and objective speech quality evaluation for wideband telephony applications. First, a custom wideband noise reduction database was created that contained speech samples corrupted by different background noises at different signal to noise ratios (SNRs) and processed by six different noise reduction algorithms. Comprehensive subjective evaluation of this database revealed an interaction between the algorithm performance, noise type and SNR. Several auditory-based objective metrics such as the Loudness Pattern Distortion (LPD) measure based on the Moore - Glasberg auditory model were evaluated in predicting the subjective scores. In addition, the performance of Bayesian Multivariate Regression Splines(BMLS) was also evaluated in terms of mapping the scores calculated by the objective metrics to the true quality scores. The combination of LPD and BMLS resulted in high correlation with the subjective scores and was used as a substitution for fine - tuning the noise reduction algorithms.
Second, the effect of echo and delay on the wideband speech was evaluated in both listening and conversational context, through both subjective and objective measures. A database containing speech samples corrupted by echo with different delay and frequency response characteristics was created, and was later used to collect subjective quality ratings. The LPD - BMLS objective metric was then validated using the subjective scores.
Third, to evaluate the effect of echo and delay in conversational context, a realtime simulator was developed. Pairs of subjects conversed over the simulated system and rated the quality of their conversations which were degraded by different amount of echo and delay. The quality scores were analysed and LPD+BMLS combination was found to be effective in predicting subjective impressions of quality for condition-averaged data
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesting dialogues, a need which led traditional NLP models of
dialogue to draw on reinforcement learning. In this paper, we show how to
integrate these goals, applying deep reinforcement learning to model future
reward in chatbot dialogue. The model simulates dialogues between two virtual
agents, using policy gradient methods to reward sequences that display three
useful conversational properties: informativity (non-repetitive turns),
coherence, and ease of answering (related to forward-looking function). We
evaluate our model on diversity, length as well as with human judges, showing
that the proposed algorithm generates more interactive responses and manages to
foster a more sustained conversation in dialogue simulation. This work marks a
first step towards learning a neural conversational model based on the
long-term success of dialogues
- …