Acoustic speech recognition model by neural net equation with competition and cooperation
نویسندگان
چکیده
A sample of 209 adults ranging from 20 to 79 years of age were studied to measure speech communication profiles as a function of age in persons who did not identify themselves as hearing impaired. The study was conducted in order to evaluate age-related speech percepton abilities and ccammmication profiles in a population who do not present for hearing assessment and who are not included in census statistics as having hearing problems. Audiometric assessment, demographic and hearing history self-reports, speech reception thresholds, consonant discrimination perception in quiet and noise, and the Ccumnunication Profile for the Hearing Impaired (CPHI) were the in.ements used to develop speech communication profiles. Hearing performance decreased with increased age. However, despite self-reports of no hearing impairment, many subjects over age 50 had audiometric thresholds that indicated hearing impairment. The responses to the CPHI were correlated to audiometric thresholds, but also to the age of the respondent, when hearing thresholds had been controlled statistically. A comparison of CPHI responses f?om this study and that of two other samples in clinical populations revealed only slightly different patterns of behaviour in the present sample when co&o&d with communication difficulties.
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 کاملشبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملApplication of pattern recognition neural network model to hearing system for continuous speech
The two or three layered networks 2LNN, 3LNN which originate from stereovision neural network are applied to speech recognition. To accommodate sequential data flow, we consider a window to which new acoustic data enter and from which final neural activities are output. Inside the window recurrent neural network develops neural activity toward a stable point. The process is called Winner-Take-A...
متن کاملAllophone-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 کاملHMM Speech Recognition with Neural Net Discrimination
Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) speech recognizers. Both attempt to improve speech pattern discrimination while retaining the temporal processing advantages of HMMs. One approach used neural nets to provide second-stage discrimination following an HMM recognizer. On a small vocabulary task, Radial Basis Function (RBF) and back-p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998