# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
###### Abstract
The vector is composed of the units calculated in the spectrum and the surrounding units, so the spectrum is transformed from a flat matrix at the beginning to a matrix-like a cube; thus, one dimension is added. Next, the transformed matrix is divided into two categories using k-means clustering, where the number of speakers is assumed to be two. The k-means clustering method is based on the principle of aggregating similar vectors and the result of clustering produces two sets of masks, which will be multiplied by the original input signal to produce two separate voice signals. TasNet or the time-domain audio-separation network has two main frameworks: Bi-LSTM and CNN. The original one, which uses Bi-LSTM as the core has a long training time because of the LSTM; thus, CNN was later added to improve the original architecture. TasNet is divided into three main parts: encoder, separator, and decoder. Initially, the sound signal is generated as a matrix by the encoder, which is like a Fourier transform that converts the sound signal into a spectrum; the difference is that the three parts of TasNet are all trained. The encoder output matrix is generated by the separator mask, which is similar to the speaker separation classical framework. Finally, the mask is multiplied with the encoder output and input into the decoder to obtain the separated sound signal. The idea of permutation invariant training is to first give the speaker separation model and calculate different losses through different output permutations and then, continue training the model after deducing a correct permutation from the losses. Therefore, a separation model is first initialized randomly to obtain a permutation; then, the model is updated and iterated until convergence. This method still needs to be executed with a known number of talkers, but it can already be used for end-to-end purposes. Automatic speech recognition (ASR) is used to capture the acoustic signal of speech and determine the spoken words by a pattern-matching method. ASR is efficiently and accurately to convert voice signal content into its corresponding text. Its function is a little different from the above-mentioned speaker separation models since they deal with the speaker's vocal signal, while the ASR analyzes the vocabulary content of the voice signal. The natural communication between machine and human can be enhanced by ASR. The acoustic model (AM) is commonly thought of as the modeling of sound generation. In general, the AM is used as the basic unit for automatic speech recognition, converting speech signals into acoustic states. More precisely, it is the probability of converting a certain acoustic symbol. AM mostly uses the HMM [7], which has a good mathematical structure and is the theoretical basis for the formation of a large number of applications. The HMM is the internal state of this Markov model, while the external world only seesthe output values at each moment, which are the acoustic symbols (acoustic features) for automatic speech recognition. The pronunciation model contains all the sets of words that need to be processed and their pronunciations; thus, it is also known as the pronunciation dictionary. Normally, the Viterbi algorithm is an efficient approach to decode the acoustic feature probabilities output by the HMM and outputs them as appropriate word strings with a maximum probability. The purpose of the language model is to combine the output of the pronunciation model into reasonable sentences according to the logic and probability of language usage. It converts a string of letters into a sentence that people can understand. Traditional automatic speech recognition, as mentioned above, is trained separately using different training data, tuning parameters, optimization strategies, and loss of functions that are not directly transferable, which can be improved with machine learning. In recent years, the development of machine learning has boomed significantly, which has led to its application in the field of speech.