ABSTRACT :
This project is based on converting the audio signals receiver to text using speech to text API. Speech to text conversion comprises of small, medium and large vocabulary conversions. Such systems process or accept the voice which then gets converted to their respective text. This paper gives a comparative analysis of the technologies used in small, medium, and large vocabulary Speech Recognition System. The comparative study determines the benefits and liabilities of all the approaches so far. The experiment shows the role of language model in improving the accuracy of speech to text conversion system. We experiments the speech data with noisy sentences and incomplete words. The results show a prominent result for randomly chosen sentences compared to sequential set of sentences.
EXISTING SYSTEM :
This approach should be capable to recognize the speech and convert the input audio into text. Likewise, this problem related to several problems. Speech recognition is an interesting application of digital signal processing which has real world applications. This method is also used in automation of many tasks which previously needed the human interaction, like identifying spoken commands to perform things like closing a door or switching on lights.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2)low Efficiency
PROPOSED SYSTEM :
Tensor layer was replaced with single sigmoid hidden layer by Hutchinson, Deng and Yu in the stacking networks. The performance was worst when the configuration in which only the bottom (first) layer was replaced with the DP layer. The performance was best and achieved more than 1% absolute reduction over the DNN when the configurations replaced the top hidden layer with the DP layer performs. This concludes the DP layers are suited to perform on binary features, consistent in findings from.
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2) High efficiency
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
