SPOKEN LANGUAGE UNDERSTANDING SYSTEM AND METHOD USING RECURRENT NEURAL NETWORKS

• Main Authors: Sengupta, Shubhashis, Mishra, Gurudatta, Ravilla, Tirupal Rao, Wabgaonkar, Harshawardhan Madhukar, Patil, Sumitraj Ganapat, Ramnani, Roshni Ramesh, Debnath, Poulami, Mahato, Moushumi, M, Sushravya G, Firdaus, Mauajama
• Format: Patent
• Language: English
• Published: 19.12.2019
• Subjects: ACOUSTICS; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; MUSICAL INSTRUMENTS; PHYSICS; SPEECH ANALYSIS OR SYNTHESIS; SPEECH OR AUDIO CODING OR DECODING; SPEECH OR VOICE PROCESSING; SPEECH RECOGNITION

A system and method for spoken language understanding using recurrent neural networks ("RNNs") is disclosed. The system and method jointly performs the following three functions when processing the word sequence of a user utterance: (1) classify a user's speech act into a dialogue act category, (2) identify a user's intent, and (3) extract semantic constituents from the word sequence. The system and method includes using a bidirectional RNN to convert a word sequence into a hidden state representation. By providing two different orderings of the word sequence, the bidirectional nature of the RNN improves the accuracy of performing the above-mentioned three functions. The system and method includes performing the three functions jointly. The system and method uses attention, which improves the efficiency and accuracy of the spoken language understanding system by focusing on certain parts of a word sequence. The three functions can be jointly trained, which increases efficiency.