Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. Aspects of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. Different speech processing tasks include speech recognition, speech synthesis, speaker diarization, speech enhancement, speaker recognition, etc. History Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple phonetic elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balashek, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker. Pioneering works in field of speech recognition using analysis of its spectrum were reported in 1940s. Linear predictive coding (LPC), a speech processing algorithm, was first proposed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966. Further developments in LPC technology were made by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the 1970s. LPC was the basis for voice-over-IP (VoIP) technology, as well as speech synthesizer chips, such as the Texas Instruments LPC Speech Chips used in the Speak & Spell toys from 1978. One of the first commercially available speech recognition products was Dragon Dictate, released in 1990. In 1992, technology developed by Lawrence Rabiner and others at Bell Labs was used by AT&T in their Voice Recognition Call Processing service to route calls without a human operator. By this point, the vocabulary of these systems was larger than the average human vocabulary. By the early 2000s, the dominant speech processing strategy started to shift away from Hidden Markov Models towards more modern neural networks and deep learning. Techniques Dynamic time warping Dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. In general, DTW is a method that calculates an optimal match between two given sequences (e.g. time series) with certain restriction and rules. The optimal match is denoted by the match that satisfies all the restrictions and the rules and that has the minimal cost, where the cost is computed as the sum of absolute differences, for each matched pair of indices, between their values. Hidden Markov models A hidden Markov model can be represented