Linguistic and acoustic features depending on different situations - the experiments considering speech recognition rate

نویسندگان

  • Shinya Yamada
  • Toshihiko Itoh
  • Kenji Araki
چکیده

This paper presents the characteristic differences of linguistic and acoustic features observed in different spoken dialogue situations and with different dialogue partners: human-human vs. human-machine interactions. We compare the linguistic and acoustic features of the user’s speech to a spoken dialogue system and to a human operator in several goal setting and destination database searching tasks for a car navigation system. It has been pointed out that speech-based interaction has the potential to distract the driver’s attention and degrade safety. On the other hand, it is not clear enough whether different dialogue situations and different dialogue partners cause any differences of linguistic or acoustic features on one’s utterances in a speech interface system. Additionally, research about influence of speech recognition rate is not enough either. We collected a set of spoken dialogues by 12 subject speakers for each experiment under several dialogue situations. For a car driving situation, we prepared a virtual driving simulation system. We also prepared two patterns where we have two dialogue partners with different speech recognition rate (100% and about 80%). We analyzed the characteristic differences of user utterances caused by different dialogue situations and with different dialogue partners in two above mentioned patterns.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Persian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods

Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...

متن کامل

A Study of the Relationship between Acoustic Features of “bæle” and the Paralinguistic Information

Language users benefit from special phonetic tools in order to communicate linguistic information as well as different emotional aspects and paralinguistic information through daily conversation. Having functions in conveying semantic information to listeners, prosodic features form the essential part of linguistic behavour, manipulating  them potentially can play an important role in transmitt...

متن کامل

Allophone-based acoustic modeling for Persian phoneme recognition

Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects o...

متن کامل

Significance of group delay based acoustic features in the linguistic search space for robust speech recognition

In this paper we discuss the complementarity of the group delay features with respect to other conventional acoustic features and also propose the use of such diverse information in the linguistic search space for robust speech recognition. A discriminability analysis is carried out on various classes of phonetic units. A class based phonetic unit analysis is conducted to compare the suitabilit...

متن کامل

Classification of emotional speech using spectral pattern features

Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005