Other

Interactive Intelligence Group, Inc.

Genesys Telecommunications Laboratories, Inc.

Genesys Cloud Services, Inc.

Interactive Intelligence, Inc.

Greeneden U.S. Holdings Ii, LLC

Aravind Ganapathiraju

Felix Immanuel Wyss

Ananth Nagaraja Iyer

Srinath Cheluvaraja

Vivek Tyagi

Yingyi Tan

Rajesh Dachiraju

E Veera Raghavendra

Scott Allen Randal

Zhenhao Ge

Ramasubramanian Sundaram

Kevin Vlack

Maciej Dabrowski

Emir Munoz

Pavan Buduguppa

Rivarol Vergin

Konstantin Biatov

Ram Sundaram

Sapna Negi

Veera Elluru Raghavendra

Elluru Veera Raghavendra

Tejas Godambe

Ananth Nagaraja Lyer

Kevin Charles Vlack

A system and method are presented for model derivation for entity prediction. An LSTM with 100 memory cells is used in the system architecture. Sentences are truncated and provided with feature information to a named-entity recognition model. A forward and a backward pass of the LSTM are performed, and each pass is concatenated. The concatenated bi-directional LSTM encodings are obtained for the various features for each word. A fully connected set of neurons shared across all encoded words is obtained and the final encoded outputs with dimensions equal to the number of entities is determined.

System and method for model derivation for entity prediction

A system and method are presented for the correction of packet loss in audio in automatic speech recognition (ASR) systems. Packet loss correction, as presented herein, occurs at the recognition stage without modifying any of the acoustic models generated during training. The behavior of the ASR engine in the absence of packet loss is thus not altered. To accomplish this, the actual input signal may be rectified, the recognition scores may be normalized to account for signal errors, and a best-estimate method using information from previous frames and acoustic models may be used to replace the noisy signal.

System and method to correct for packet loss in ASR systems

A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.

System and method for customer journey event representation learning and outcome prediction using neural sequence models

A system and method are presented for F0 transfer learning for improving F0 prediction with deep neural network models. Larger models are trained using long short-term memory (LSTM) and multi-layer perceptron (MLP) feed-forward hidden layer modeling. The fundamental frequency values for voiced and unvoiced segments are identified and extracted from the larger models. The values for voiced regions are transferred and applied to training a smaller model and the smaller model is applied in the text to speech system for real-time speech synthesis output.

System and method for F0 transfer learning for improving F0 prediction with deep neural network models

A system and method are presented for the automatic filtering of test utterance mismatches in automatic speech recognition (ASR) systems. Test data are evaluated for match between audio and text in a language-independent manner. Utterances having mismatch are identified and isolated for either removal or manual verification to prevent incorrect measurements of the ASR system performance. In an embodiment, contiguous stretches of low probabilities in every utterance are searched for and removed. Such segments may be intra-word or cross-word. In another embodiment, scores may be determined using log DNN probability for every word in each utterance. Words may be sorted in the order of the scores and those utterances containing the least word scores are removed.

System and method for automatic filtering of test utterance mismatches in automatic speech recognition systems

A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.

System and method for a multiclass approach for confidence modeling in automatic speech recognition systems

A method for automated generation of contact center system embeddings according to one embodiment includes determining, by a computing system, contact center system agents, contact center system agent skills, and/or contact center system virtual queue experiences; generating, by the computing system, a matrix representation based on the contact center system agents, the contact center system agent skills, and/or the contact center system virtual queue experiences; generating, by the computing system and based on the matrix representation, contact center system agent identifiers, contact center system agent skills identifiers, and/or contact center system virtual queue identifiers; transforming, by the computing system, the contact center system agent identifiers, the contact center system agent skills identifiers, and/or the contact center system virtual queue identifiers into the contact center system agent embeddings, contact center system agent skills embeddings, and/or contact center system virtual queue embeddings, wherein weights of the contact center system agent embeddings, the contact center system agent skills embeddings, and/or the contact center system virtual queue embeddings are randomly initialized; and training, by the computing system, the contact center system agent embeddings, the contact center system agent skills embeddings, and/or the contact center system virtual queue embeddings by applying machine learning to obtain final weights of the contact center system agent embeddings, the contact center system agent skills embeddings, and/or the contact center system virtual queue embeddings.

Technologies for automated generation of contact center system embeddings

System and method to correct for packet loss using hidden markov models in ASR systems

A method for classifying speakers includes: receiving, by a speaker recognition system including a processor and memory, input audio including speech from a speaker; extracting, by the speaker recognition system, a plurality of speech frames containing voiced speech from the input audio; computing, by the speaker recognition system, a plurality of features for each of the speech frames of the input audio; computing, by the speaker recognition system, a plurality of recognition scores for the plurality of features; computing, by the speaker recognition system, a speaker classification result in accordance with the recognition scores; and outputting, by the speaker recognition system, the speaker classification result.

System and method for neural network based speaker classification

A system and method are presented for the synthesis of speech from provided text. Particularly, the generation of parameters within the system is performed as a continuous approximation in order to mimic the natural flow of speech as opposed to a step-wise approximation of the feature stream. Provided text may be partitioned and parameters generated using a speech model. The generated parameters from the speech model may then be used in a post-processing step to obtain a new set of parameters for application in speech synthesis.

System and method for synthesis of speech from provided text

A system and method are presented for forming the excitation signal for a glottal pulse model based parametric speech synthesis system. The excitation signal may be formed by using a plurality of sub-band templates instead of a single one. The plurality of sub-band templates may be combined to form the excitation signal wherein the proportion in which the templates are added is dynamically based on determined energy coefficients. These coefficients vary from frame to frame and are learned, along with the spectral parameters, during feature training. The coefficients are appended to the feature vector, which comprises spectral parameters and is modeled using HMMs, and the excitation signal is determined.

Method for forming the excitation signal for a glottal pulse model based parametric speech synthesis system

Technologies for authenticating a speaker in a voice authentication system using voice biometrics include a speech collection computing device and a speech authentication computing device. The speech collection computing device is configured to collect a speech signal from a speaker and transmit the speech signal to the speech authentication computing device. The speech authentication computing device is configured to compute a speech signal feature vector for the received speech signal, retrieve a speech signal classifier associated with the speaker, and feed the speech signal feature vector to the retrieved speech signal classifier. Additionally, the speech authentication computing device is configured to determine whether the speaker is an authorized speaker based on an output of the retrieved speech signal classifier. Additional embodiments are described herein.

Technologies for authenticating a speaker using voice biometrics

A method for training a neural network of a neural network based speaker classifier for use in speaker change detection. The method comprises: a) preprocessing input speech data; b) extracting a plurality of feature frames from the preprocessed input speech data; c) normalizing the extracted feature frames of each speaker within the preprocessed input speech data with each speaker's mean and variance; d) concatenating the normalized feature frames to form overlapped longer frames having a frame length and a hop size; e) inputting the overlapped longer frames to the neural network based speaker classifier; and f) training the neural network through forward-backward propagation.

System and method for speaker change detection

A system and method are presented for outlier identification to remove poor alignments in speech synthesis. The quality of the output of a text-to-speech system directly depends on the accuracy of alignments of a speech utterance. The identification of mis-alignments and mis-pronunciations from automated alignments may be made based on fundamental frequency methods and group delay based outlier methods. The identification of these outliers allows for their removal, which improves the synthesis quality of the text-to-speech system.

System and method for outlier identification to remove poor alignments in speech synthesis

A system and method are presented for predicting speech recognition performance using accuracy scores in speech recognition systems within the speech analytics field. A keyword set is selected. Figure of Merit (FOM) is computed for the keyword set. Relevant features that describe the word individually and in relation to other words in the language are computed. A mapping from these features to FOM is learned. This mapping can be generalized via a suitable machine learning algorithm and be used to predict FOM for a new keyword. In at least embodiment, the predicted FOM may be used to adjust internals of speech recognition engine to achieve a consistent behavior for all inputs for various settings of confidence values.

Growing community of inventors

Hyderabad Telangana, India

Aravind Ganapathiraju