Microsoft Technology Licensing, LLC

Changliang Liu

Jinyu Li

Yifan Gong

Liang Lu

Zhuo Chen

Takuya Yoshioka

Dimitrios Basile Dimitriadis

Xiong Xiao

Hakan Erdogan

Amit Kumar Agarwal

Jian Wu

Kshitiz Kumar

Edward C Lin

Amit Das

Vadim Aleksandrovich Mazalov

Tianyu Wu

Bo Ren

Fahimeh Bahmaninezhad

Xiaoyang Chen

Harini Kesavamoorthy

A data processing system is implemented for receiving speech data for a plurality of languages, and determining letters from the speech data. The data processing system also implements normalizing the speech data by applying linguistic based rules for Latin-based languages on the determined letters, building a computer model using the normalized speech data, fine-tuning the computer model using additional speech data, and recognizing words in a target language using the fine-tuned computer model.

Automatic speech recognition systems and processes

Representative embodiments disclose machine learning classifiers used in scenarios such as speech recognition, image captioning, machine translation, or other sequence-to-sequence embodiments. The machine learning classifiers have a plurality of time layers, each layer having a time processing block and a depth processing block. The time processing block is a recurrent neural network such as a Long Short Term Memory (LSTM) network. The depth processing blocks can be an LSTM network, a gated Deep Neural Network (DNN) or a maxout DNN. The depth processing blocks account for the hidden states of each time layer and uses summarized layer information for final input signal feature classification. An attention layer can also be used between the top depth processing block and the output layer.

Machine learning model with depth processing units

According to some embodiments, a universal modeling system may include a plurality of domain expert models to each receive raw input data (e.g., a stream of audio frames containing speech utterances) and provide a domain expert output based on the raw input data. A neural mixture component may then generate a weight corresponding to each domain expert model based on information created by the plurality of domain expert models (e.g., hidden features and/or row convolution). The weights might be associated with, for example, constrained scalar numbers, unconstrained scaler numbers, vectors, matrices, etc. An output layer may provide a universal modeling system output (e.g., an automatic speech recognition result) based on each domain expert output after being multiplied by the corresponding weight for that domain expert model.

Universal acoustic modeling using neural mixture models

According to some embodiments, a machine learning model may include an input layer to receive an input signal as a series of frames representing handwriting data, speech data, audio data, and/or textual data. A plurality of time layers may be provided, and each time layer may comprise a uni-directional recurrent neural network processing block. A depth processing block may scan hidden states of the recurrent neural network processing block of each time layer, and the depth processing block may be associated with a first frame and receive context frame information of a sequence of one or more future frames relative to the first frame. An output layer may output a final classification as a classified posterior vector of the input signal. For example, the depth processing block may receive the context from information from an output of a time layer processing block or another depth processing block of the future frame.

Layer trajectory long short-term memory with future context

A system and method include reception of a first plurality of audio signals, generation of a second plurality of beamformed audio signals based on the first plurality of audio signals, each of the second plurality of beamformed audio signals associated with a respective one of a second plurality of beamformer directions, generation of a first TF mask for a first output channel based on the first plurality of audio signals, determination of a first beamformer direction associated with a first target sound source based on the first TF mask, generation of first features based on the first beamformer direction and the first plurality of audio signals, determination of a second TF mask based on the first features, and application of the second TF mask to one of the second plurality of beamformed audio signals associated with the first beamformer direction.

Low-latency speech separation

Streaming machine learning unidirectional models is facilitated by the use of embedding vectors. Processing blocks in the models apply embedding vectors as input. The embedding vectors utilize context of future data (e.g., data that is temporally offset into the future within a data stream) to improve the accuracy of the outputs generated by the processing blocks. The embedding vectors cause a temporal shift between the outputs of the processing blocks and the inputs to which the outputs correspond. This temporal shift enables the processing blocks to apply the embedding vector inputs from processing blocks that are associated with future data.

Streaming contextual unidirectional models

Growing community of inventors

Bothell, WA, United States of America

Changliang Liu