Hybrid Speech Recognition for Voice Search: A Comparative Study
نویسنده
چکیده
We compare different systems for use in information retrieval of items by voice. These systems differ only in the unit they use: words, a subwords, a combination of these into a hybrid, and phones. The subword set is derived by splitting words using a Minimum Description Length (MDL) criterion. In general, we convert an index written in terms of words into an index written in terms of these different units. A speech recognition engine that uses a language model and pronunciation dictionary built from each such an inventory of units is completely independent from the information retrieval task, and can, therefore, remain fixed, making this approach ideal for resource constrained systems. We demonstrate that recognition accuracy and recall results at higher OOV rates are much superior for the hybrid system than the alternatives. On a music lyrics task at 80% OOV, the hybrid system has a recall of 82.9%, compared to 75.2% for the subword-based one and 47.4% for a word system.
منابع مشابه
Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملA Comparative Study of Gender and Age Classification in Speech Signals
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...
متن کاملA hybrid HMM/traps model for robust voice activity detection
We present three voice activity detection (VAD) algorithms that are suitable for the off-line processing of noisy speech and compare their performance on SPINE-2 evaluation data using speech recognition error rate as the quality metric. One VAD system is a simple HMM-based segmenter that uses normalized log-energy and a degree of voicing measure as raw features. The other two VAD systems focus ...
متن کاملVoice-based Age and Gender Recognition using Training Generative Sparse Model
Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...
متن کاملA preliminary study on improving the recognition of esophageal speech using a hybrid system based on statistical voice conversion
In this paper, we propose a hybrid system based on a modified statistical GMM voice conversion algorithm for improving the recognition of esophageal speech. This hybrid system aims to compensate for the distorted information present in the esophageal acoustic features by using a voice conversion method. The esophageal speech is converted into a "target" laryngeal speech using an iterative stati...
متن کامل