Periagaram K. Rajasekaran

Speaker-independent word recognition method and system based upon zero-crossing rate and energy measurement of analog speech signal

Speaker-independent word recognition method and system for identifying individual spoken words based upon an acoustically distinct vocabulary of a limited number of words. The word recognition system may employ memory storage associated with a microprocessor or microcomputer in which reference templates of digital speech data representative of a limited number of words comprising the word vocabulary are stored. The word recognition system accepts an input analog speech signal from a microphone as derived from a single word-voice command spoken by any speaker. The analog speech signal is directed to an energy measuring circuit and a zero-crossing detector for determining a sequence of feature vectors based upon the zero-crossing rate and energy measurements of the sampled analog speech signal. The sequence of feature vectors are then input to the microprocessor or microcomputer for individual comparison with the feature vectors included in each of the reference templates as stored in the memory portion of the microprocessor or microcomputer. Comparison of the sequence of feature vectors as determined from the input analog speech signal with the feature vectors included in the plurality of reference templates produces a cumulative cost profile for enabling logic circuitry within the microprocessor or microcomputer to make a decision as to the identity of the spoken word. The work recognition system may be incorporated within an electronic device which is also equipped with speech synthesis capability such that the electronic device is able to recognize simple words as spoken thereto and to provide an audible comment via speech synthesis which is related to the spoken word.

Low cost speech recognition system and method

A low cost speech recognition system generates frames of received speech having binary feature components. The received speech frames are compared with reference templates, and error values representing the difference between the received speech and the reference templates are generated. At the end of an utterance, if one template resulted in a sufficiently small error value, the word represented by that template is selected as the recognized word.

Method of encoding speech signals involving the extraction of speech formant candidates in real time

Method of encoding speech signals which is based upon determining the roots of the linear prediction polynomial describing the spectrum of an analog speech signal, wherein the roots are candidates in determining the formants of the speech signal. The method involves the analysis of respective frames of sampled digital speech data using a linear predictive technique to determine a set of reflection coefficients or K-parameters which are then converted into the equivalent predictor coefficients or A-parameters describing a prediction polynomial having a plurality of roots corresponding to the poles of an all-pole filter characterizing the vocal tract. A modified Bairstow technique is then empolyed for factoring out quadratic factors which are then sorted in an ordered arrangement in terms of ascending bandwidths. In performing the modified Bairstow technique, initial estimates of the successive quadratic factors for a current frame of digital speech data are made in sequence, and the prediction polynomial is successively deflated to a reduced order polynomial in determining the respective quadratic factors thereof. The initial estimate of the first quadratic factor is the same as the smallest bandwidth root as determined from the previous frame of digital speech data. These removed quadratic factors or roots are candidates for determining the formants of the speech signal.

Robustness study of free-text speaker identification and verification

Usable free-text speaker identification and voice verification systems must exhibit robustness under varying operational conditions. The authors study the degree of robustness provided by various signal processing techniques by experimenting on a widely used long distance telephone database. This database consists of data recorded at two different sites, with data from one site much poorer in quality than that from the other. Further, the recording equipment had been inadvertently changed for the later half of the sessions, resulting in a significantly changed environment. The combination of techniques that provide consistent and significant improvements is identified. The present results surpass other published results on the same task. Specifically, in the task of identifying 16 speakers with training data from the recording prior to equipment change and testing on data from a set after the change (the most challenging condition), a correct identification rate of 87.5% with an average rank of 1.12 was obtained.<<ETX>>

Toward vocabulary independent telephone speech recognition

Vocabulary-independence of speech recognition systems has become an important issue because of the need for flexible vocabulary and the high cost of speech corpus collection. We outline the necessary steps to achieve the goal of vocabulary-independent speech recognition, and relate our experimental experience with telephone speech recognition. Two sets of experiments were conducted: (1) 34-command recognition, in which we compared vocabulary-independent (VI) and vocabulary-dependent (VD) systems as well as phonetic and word based systems, and (2) 42-city name recognition, in which our vocabulary independent recognition performance (8.5% W. Err.) was much better than the VI performance (18%) reported by the Oregon Graduate Institute (OGI) and very close to OGIs VD performance (8%). We conclude that we have made some strides toward vocabulary independence, but much remains to be done; we identify the areas of improvement that are likely to lead to the goal.<<ETX>>

Free-text speaker identification over long distance telephone channel using hypothesized phonetic segmentation

Experimental investigation of a free-text speaker identification method based on long-term statistics was conducted on a widely used long-distance telephone database. On a 26-speaker subset, an average correct identification of 93.3% was obtained; on the complete 51-speaker set, 67.6% correct identification is obtained. Speaker verification experiments on the database provided receiver operating characteristics (ROCs) comparable to or better than the ones available in open literature.<<ETX>>

Microcomputer implementable low cost speaker-independent word recognition

A simple algorithm to perform speaker-independent word recognition with modest performance on a small, acoustically distinct, vocabulary is described. The primary measurements are the zero crossing intervals of the acoustic waveform. Robust and reliable performance was achieved by deriving binary-valued features, using dynamic time warping and invoking high level logic. The algorithm demonstrates implementation potential via simple analog circuitry and an 8-bit microcomputer. The performance achieved was 85% correct recognition, 9% rejection and 6% substitution for a set of six words uttered by over 100 speakers.

A low cost dynamic vocabulary speech recognizer on a GPP-DSP system

Continuous speech recognition is a resource-intensive algorithm. Commercial dictation software requires more than 10 Mbytes to install on the disk and 32 Mbytes RAM to run the application. Because of the resource requirement, such a system can not be implemented in a low cost and low power embedded system. We propose a design of dynamic vocabulary speech recognizer that will fit in a DSP-GPP (general purpose processor) architecture. The computation intensive, small footprint recognizer engine runs on the DSP; and the computation non-intensive, larger footprint grammar, dictionary, and model acoustic components resides on the GPP. The recognition models are prepared on the GPP and transferred to the DSP, the interaction among the application, model generation, and recognition modules is minimal. The result is a speech recognition server implemented in a low cost embedded system. The application can dynamically create flexible vocabulary to suit different recognition contexts. It still does not do large vocabulary dictation; however, it provides unlimited recognition contexts with unlimited vocabulary, all these implementable in a low cost embedded system.

Speaker Recognition Over Telephone Channels

Being able to verify or determine the identity of a person by voice is very useful in many applications. For example, in telephone banking or calling card charging, user identity must be verified before the transaction can be authorized. Most systems use a PIN for authorization, but it can be forgotten or stolen. Other methods of authorization, such as finger prints or retinal scans, may be more secure than a PIN, they are not practical in many situations.

Connected word recognizer on a multiprocessor system

Speech recognition algorithms employing a similarity measure between the input speech utterance and the stored reference patterns to determine recognition of a word/sentence are computationally intensive. The instantaneous vocabulary size that can be handled in real-time is relatively small. This limitation can be alleviated by either using multiple programmable processors or by using special purpose hardware to handle the computation-intensive tasks. In a research environment the former approach is preferred, because improvements to the algorithm can rapidly be incorporated and their effects studied in real-time. Texas Instruments has developed a multiple-processor architecture based on the TMS32020 DSP, called Odyssey, that interfaces with Explorer, a symbolic computer. This paper addresses the issues involved in partitioning and allocating tasks in a multiple-processor environment to maximise throughput, and discusses the implementation of a grammar-driven speaker-dependent connected-word recognizer (GDCWR) as an example application that uses the power of multiple processors.