Patent Publication Number: US-11646037-B2

Title: Sample-efficient representation learning for real-time latent speaker state characterization

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent Application No. 63/061,018, filed on Aug. 4, 2020 and entitled “SAMPLE-EFFICIENT REPRESENTATION LEARNING FOR REAL-TIME LATENT SPEAKER STATE CHARACTERISATION,” which is incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present technology relates to the field of automated speech recognition (ASR). More particularly, the present technology relates to augmented ASR techniques to capture latent speaker states. 
     BACKGROUND 
     Human language is an evolved ability that allows formulation and communication of complex thoughts. Spoken language in particular enables real-time, interactive, high-bandwidth communication of ideas across multiple actors in a conversation. This ability to seamlessly interact, debate, and collaborate was and continues to be one of the main driving forces behind the rise and ongoing development of human civilization. 
     As increasingly sophisticated and networked computer systems are built, the need to interact with such systems using natural language has become increasingly important. Conventional approaches to automatic speech recognition (ASR) have reached human parity in conversational speech recognition. However, such conventional approaches to ASR focus on the ability of a system to transform a stream of sound into a series of tokens representing the spoken words. These conventional approaches fail to consider or comprehend the way in which the words are uttered (e.g., intonation), non-verbal acoustic cues denoting latent states, and prosodic cues that embed information contained in speech. As an example, conventional ASR systems are unable to accurately and consistently distinguish the meaning of a heartfelt “thank you” versus a sarcastic “thank you”. 
     SUMMARY 
     Various embodiments of the present technology can include systems, methods, and non-transitory computer readable media configured to provide audio waveform data that corresponds to a voice sample to a temporal convolutional network for evaluation. The temporal convolutional network can pre-process the audio waveform data and can output an identity embedding associated with the audio waveform data. The identity embedding associated with the voice sample can be obtained from the temporal convolutional network. Information describing a speaker associated with the voice sample can be determined based at least in part on the identity embedding. 
     In an embodiment, determining the information describing the speaker further includes determining an identity of the speaker associated with the voice sample based at least in part on the identity embedding. 
     In an embodiment, determining the information describing the speaker further includes determining that the speaker associated with the voice sample matches a known speaker based at least in part on the identity embedding. 
     In an embodiment, the temporal convolutional network is trained based on a triplet loss function that evaluates a plurality of triplets, wherein a triplet includes an anchor voice sample, a positive voice sample that has a threshold level of similarity to the anchor voice sample, and a negative voice sample that does not have a threshold level of similarity to the anchor voice sample. 
     In an embodiment, a triplet of voice samples is selected based on semi-hard triplet mining. 
     In an embodiment, the temporal convolutional network is trained based on at least one of: contrastive loss, lifted structures loss, N-pair loss, angular loss, or SoftTriple loss. 
     In an embodiment, the temporal convolutional network embeds the voice sample in a continuous high-dimensional identity space. 
     In an embodiment, the temporal convolutional network includes a plurality of temporal convolutional neural residual blocks (TCN blocks) that pre-process the audio waveform data. 
     In an embodiment, a TCN block applies a set of temporal causal convolutions to the audio waveform data. 
     In an embodiment, a TCN block includes a pair of consecutive one-dimensional convolutional layers using causal padding, and wherein the pair of consecutive one-dimensional convolutional layers are bypassed by an additive skip connection. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1 A  illustrates an example fused deep neural network architecture, according to embodiments of the present technology. 
         FIGS.  1 B- 1 C  illustrate example methods, according to embodiments of the present technology. 
         FIG.  2    illustrates an example system, according to an embodiment of the present technology. 
         FIG.  3 A  illustrates an example neural network architecture, according to embodiments of the present technology. 
         FIG.  3 B  illustrates an example TCN block, according to embodiments of the present technology. 
         FIG.  3 C  illustrates another example TCN block, according to embodiments of the present technology. 
         FIG.  4 A  illustrates an example mapping based on an untrained temporal convolutional network. 
         FIG.  4 B  illustrates an example mapping based on a trained temporal convolutional network, according to embodiments of the present technology. 
         FIG.  5    illustrates an example of a computer system or computing device that can be utilized in various scenarios, according to embodiments of the present technology. 
     
    
    
     The figures depict various embodiments of the present technology for purposes of illustration only. The figures use like reference numerals to identify like elements. One skilled in the art will readily recognize from the following discussion that alternative and additional embodiments of the present technology apart from those illustrated in the figures can be employed without departing from the principles of the present technology described herein. 
     DETAILED DESCRIPTION 
     Conventional approaches for capturing latent speaker states from spoken conversations typically suffer from a number of limitations. For example, conventional approaches can be based on text modelling techniques, which can use transcribed text as a basis for inferring speaker states, such as emotions. Such conventional approaches are less accurate due to the inherently lossy nature of automated speech recognition (ASR), which does not capture speech intonation. These conventional approaches are also language-dependent and thus require extensive training for every new language or dialect. In another example, conventional approaches can use spectral representations or derived representations (e.g., MFCCs) of an audio signal, which can require exploring and tuning a number of parameters, such as the width and overlap of signal windows, or the number of filter banks. These conventional approaches can incur a high computational cost and result in loss of information due to the use of an intermediate representation. In yet another example, conventional approaches can be undesirably trained on either small amounts of high-quality data (e.g., annotated and reviewed by humans) or larger amounts of low-quality data (e.g., data collected from the Internet with noisy labels). 
     An improved approach rooted in computer technology overcomes the foregoing and other disadvantages associated with conventional approaches specifically arising in the realm of computer technology. The present technology provides a fused deep neural network architecture that can accept a stream of sound as input (e.g., a raw audio waveform) and can output its latent representation in the form of an identity embedding (or vector). The present technology can thus learn a representation directly from a raw waveform with no spectral transformation or language transcription needed. The fused deep neural network architecture includes a number of components, such as a pre-processing stage, a similarity learning process, and a speaker state classification stage. In various embodiments, all components of the fused deep neural network architecture can be defined through the same code, which allows the fused deep neural network architecture to be packaged into a single, unified model file. In contrast, conventional architectures typically comprise several individual components that each require separate training and tuning, thereby increasing deployment and maintenance costs. The present technology thus provides an end-to-end model that can process a voice sample as input and can output an identity vector in response. Identity vectors can be modeled for various applications. For example, in some embodiments, identity vectors can be used to generate identity-based pretrained markers. The identity-based pretrained markers can be used to detect various identity-based attributes (e.g., gender, age, etc.) from speech. In some embodiments, identity vectors can be used to generate language-based pretrained markers. The language-based pretrained markers can be used to detect various language-based attributes (e.g., spoken language, accent, etc.) from speech. More details relating to the present technology are provided below. 
       FIG.  1 A  illustrates an example fused deep neural network architecture  100 , according to an embodiment of the present technology. The example fused deep neural network architecture  100  can accept a stream of sound (e.g., a raw audio waveform) as input  102 . For example, the architecture  100  can accept an uncompressed (PCM) audio signal sampled at 16 kHz as input. At block  104 , the input  102  can be pre-processed by a convolutional neural network. For example, in some embodiments, the input  102  can be pre-processed by a temporal convolutional network, as described below. At block  106 , similarity learning techniques can be applied to map an identity space or an embedding space (e.g., Euclidean space) based on a learned distance function. Based on similarity learning, the temporal convolutional network can be trained to generate identity embeddings  108  that provide latent representations of inputted audio waveforms. In general, identity embeddings of voice samples associated with the same speaker appear closer together in the identity space than identity embeddings of voice samples associated with other speakers. At block  110 , identity embeddings can be analyzed based on supervised learning techniques. At block  112 , identity-based pretrained markers can be generated based on the identity embeddings analyzed at block  110 . As shown, identity-based pretrained markers can be applied to detect speaker emotion, arousal, age, and gender based on voice samples. As shown, identity-based pretrained markers also can be applied to detect speech, music, and human sounds. As shown, identity-based pretrained markers also can be applied to detect custom markers. At block  120 , pre-processed output from block  104  can be analyzed based on supervised learning techniques. At block  122 , language-based pretrained markers can be generated based on the identity embeddings analyzed at block  120 . As shown, language-based pretrained markers can be applied to detect speech, spoken language, native language, and accent based on voice samples. Many variations are possible. More details describing various aspects of the fused deep neural network architecture  100  are provided herein. 
       FIG.  1 B  illustrates an example method  150 , according to an embodiment of the present technology. In some embodiments, the method  150  can be performed by a system  200  of  FIG.  2   . At block  152 , an anchor sample can be determined based on a first voice sample from a speaker. At block  154 , a positive sample can be determined based on a second voice sample from the same speaker. At block  156 , a negative sample can be determined based on a third voice sample from a different speaker. In some embodiments, the samples can be selected through a semi-hard triplet mining process. Under this process, a triplet of samples can be selected so a negative sample is not closer to an anchor sample than a positive sample but still has positive loss, for example, due to a margin parameter. At block  158 , a convolutional neural network (e.g., a temporal convolutional network) can be trained based on a triplet loss function that evaluates the anchor sample, the positive sample, and the negative sample. The result is an identity space, similar to a high-dimensional map in which voice samples from the same speaker are very close together while voice samples from distinct speakers are far apart. Because the model is trained on triplets of samples rather than individual samples, the amount of data available to a system during training increases combinatorially with the number of samples and distinct speakers in the training set. While not all triplets are suitable for training, this makes it possible to train an identity model on a relatively small amount of data, making the learned representation very sample-efficient. Many variations to the example methods are possible. It should be appreciated that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments discussed herein unless otherwise stated. 
       FIG.  1 C  illustrates an example method  170 , according to an embodiment of the present technology. In some embodiments, the method  170  can be performed by the system  200  of  FIG.  2   . At block  172 , audio waveform data that corresponds to a voice sample can be provided to a temporal convolutional network for evaluation. The temporal convolutional network can pre-process the audio waveform data and output an identity embedding associated with the audio waveform data. At block  174 , the identity embedding associated with the voice sample can be obtained from the temporal convolutional network. At block  176 , information describing a speaker associated with the voice sample can be determined based at least in part on the identity embedding. Many variations to the example methods are possible. It should be appreciated that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments discussed herein unless otherwise stated. 
       FIG.  2    illustrates the example system  200 , according to an embodiment of the present technology. The example system  200  can include a convolutional neural network module  202 . The convolutional neural network module  202  can include a training module  204  and an evaluation module  206 . The training module  204  and the evaluation module  206  can be implemented in one or more software applications running on one or more computing devices. The components (e.g., modules, elements, etc.) shown in this figure and all figures herein are exemplary only, and other implementations may include additional, fewer, integrated, or different components. Some components may not be shown so as not to obscure relevant details. In various embodiments, one or more of the functionalities described in connection with the training module  204  and the evaluation module  206  can be implemented in any suitable sequences and combinations. The convolutional neural network module  202  can communicate with a data store  210 . The data store  210  can store various information needed to train and implement the convolutional neural network module  202 . For example, the data store  210  can store training data, such as triplets of voice samples, that can be used to train a convolutional neural network. 
     In some embodiments, the various modules and/or applications described herein can be implemented, in part or in whole, as software, hardware, or any combination thereof. In general, a module and/or an application, as discussed herein, can be associated with software, hardware, or any combination thereof. In some implementations, one or more functions, tasks, and/or operations of modules and/or applications can be carried out or performed by software routines, software processes, hardware, and/or any combination thereof. In some cases, the various modules and/or applications described herein can be implemented, in part or in whole, as software running on one or more computing devices or systems, such as on a server or on a user or client computing device. For example, one or more modules and/or applications, or at least a portion thereof, can be implemented using one or more computing devices or systems that include one or more servers, such as network servers or cloud servers. As another example, one or more modules and/or applications described herein, or at least a portion thereof, can be implemented as or within an application (e.g., app), a program, or an applet, etc., running on a user computing device or a client computing system. It should be understood that there can be many variations or other possibilities. 
     The training module  204  can be configured to train a convolutional neural network. For example, the training module  204  can train a temporal convolutional network to process a stream of sound as input and in response output its latent representation, for example, in the form of a fixed-size embedding (or vector). In various embodiments, the temporal convolutional network can be trained based on similarity learning. For example, the training module  204  can apply metric learning to map a continuous high-dimensional identity space (e.g., Euclidean space) with a learned distance function. For example, the training module  204  can use similarity learning to embed audio waveform representations in the identity space. The identity space can be used to chart patterns that the temporal convolutional network has not yet seen (or processed). As a result, the temporal convolutional network can accurately map new inputs or variations of known inputs in the identity space. For example, a new input can be mapped to a precise and meaningfully structured region in the identity space that is distinct from regions that include known inputs. In another example, a variation of a known input can be mapped to a region that includes or is near the known input in the identity space. The learned identity space thus facilitates zero-shot learning. 
     The training module  204  can apply various loss functions to train the temporal convolutional network. For example, in some embodiments, the training module  204  can train the temporal convolutional network based on a triplet loss technique. The triplet loss technique can learn a loss function where a baseline (or anchor) sample is compared to a positive sample and a negative sample. For example, a triplet of samples can be formed by combining an anchor sample which corresponds to a voice sample from a given speaker, a positive sample which corresponds to another voice sample from the same speaker, and a negative sample which corresponds to a voice sample from a different speaker. The voice samples representing the anchor and positive sample can satisfy a threshold level of similarity. Further, the voice samples representing the anchor and positive sample can be of the same speaker in different states. For example, the voice sample representing the anchor sample may be captured while the speaker is at rest while the voice sample representing the positive sample may be captured while the speaker is in an angry mood. The voice samples representing the anchor and negative sample can be selected so they do not satisfy a threshold level of similarity. In various embodiments, the loss function applied by the training module  204  can penalize the temporal convolutional network when a learned distance between an anchor sample and a positive sample is smaller than a learned distance between the anchor sample and a negative sample plus a margin parameter. The margin parameter value can vary depending on the implementation and normalization. Many variations are possible. 
     The training module  204  can intelligently select triplets for training the temporal convolutional network using a semi-hard triplet mining process. For example, a set of triplets can be selected so a negative sample is not more similar to an anchor sample than a positive sample is to the anchor sample. Similarity can be based on distance in the identity space. In this example, the anchor sample, positive sample, and negative sample can still be associated with a positive loss due to the margin parameter, which can help speed up convergence. Based on training, the temporal convolutional network can learn an identity space which serves as a high-dimensional map in which voice samples from the same speaker are mapped very close together while voice samples from distinct speakers are mapped farther apart. By training the temporal convolutional network using triplets of samples rather than individual samples, the amount of data available during training can increase combinatorially with the number of samples and distinct speakers in a training set. In some embodiments, the training module  204  can use spatial dropouts in the temporal convolutional network for regularization during training. 
     The training module  204  can apply other loss functions to train the temporal convolutional network. For example, the training module  204  can apply other metric learning loss functions designed to optimize latent representation learning. For example, in some embodiments, the training module  204  can implement a contrastive loss function to train the temporal convolutional network. An example approach for implementing the contrastive loss function is described in Raia Hadsell, Sum it Chopra, and Yann LeCun “Dimensionality Reduction by Learning an Invariant Mapping,”  IEEE Computer Society Conference on Computer Vision and Pattern Recognition  ( CVPR ), 2006, Vol. 2, pp. 1735-1742. In some embodiments, the training module  204  can implement a lifted structures loss function to train the temporal convolutional network. The lifted structures loss can jointly optimize the distance function over all samples in a batch instead of a single pair or triplet. An example approach for implementing the lifted structures loss function is described in Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese “Deep Metric Learning Via Lifted Structured Feature Embedding,”  Proceedings of the IEEE International Conference on Computer Vision,  2016, pp. 4004-4012. In some embodiments, the training module  204  can implement an N-pair loss function to train the temporal convolutional network. An example approach for implementing the N-pair loss function is described in Kihyuk Sohn “Improved Deep Metric Learning with Multi-class N-pair Loss Objective,”  Advances in Neural Information Processing Systems,  2016, pp. 1857-1865. In some embodiments, the training module  204  can implement an angular loss function to train the temporal convolutional network. An example approach for implementing the angular loss function is described in Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin “Deep Metric Learning with Angular Loss,”  Proceedings of the IEEE International Conference on Computer Vision,  2017, pp. 2593-2601. In some embodiments, the training module  204  can implement a SoftTriple loss function to train the temporal convolutional network. An example approach for implementing the SoftTriple loss function is described in Qi Qian, Lei Shang, Baigui Sun, Juhua Hu, Hao Li, and Rong Jin “SoftTriple Loss: Deep Metric Learning Without Triplet Sampling,”  Proceedings of the IEEE International Conference on Computer Vision,  2019, pp. 6450-6458. Again, many variations are possible. 
     The evaluation module  206  can be configured to process raw audio signals. For example, the evaluation module  206  can process a raw audio signal (e.g., audio waveform) as input and can output a corresponding latent representation of the raw audio signal in the form of an identity embedding (or vector). In various embodiments, the evaluation module  206  can be configured to implement a convolutional neural network as trained by the training module  204  to pre-process raw audio signals (e.g., raw waveforms). As background, conventional approaches for pre-processing audio typically rely on a front-end that accepts a raw audio signal as input. The front-end can extract features from the raw audio signal (e.g., Mel Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), Line Spectral Frequencies (LSFs), Discrete Wavelet Transform (DWT), and Perceptual Linear Prediction (PLP)). The extracted features can be provided to a classifier for further processing. These conventional approaches to pre-processing generally suffer from a number of disadvantages, such a high computational cost, a relatively large number of tunable parameters, and invariance to the data that goes through the front-end. That is, the front-end feature extractor is designed to extract a pre-defined and static set of features, and therefore is unable to learn to extract pertinent features. In contrast, the convolutional neural network implemented by the evaluation module  206  allows for pre-processing audio waveforms and determining corresponding identity embeddings in parallel while keeping a low memory footprint both at training and at inference unlike conventional approaches, such as conventional recurrent architectures. In some embodiments, the evaluation module  206  can implement a temporal convolutional network, as illustrated in the example of  FIG.  3 A . For example, a raw audio signal can be provided to the temporal convolutional network as input. The temporal convolutional network can extract descriptive features from the raw audio signal. The extracted features can be used to learn identity embeddings. Further, the identity embeddings can be analyzed to classify speakers and states (e.g., emotions). More details describing the temporal convolutional network are provided below in reference to  FIG.  3 A . 
       FIG.  3 A  illustrates an example temporal convolutional network  300  that can be trained and implemented by the convolutional neural network module  202 , according to an embodiment of the present technology. The temporal convolutional network  300  includes an input layer, a series of temporal convolutional neural (TCN) residual blocks (“TCN blocks”), an identity embedding layer, and a normalization layer. In  FIG.  3 A , the temporal convolutional network  300  is shown with eight TCN blocks as just one example. Naturally, the number of TCN blocks can vary depending on the implementation. For example, the temporal convolutional network  300  can be implemented with four TCN blocks, six TCN blocks, or ten TCN blocks. Many variations are possible. In some embodiments, the TCN blocks are associated with a fixed kernel size (e.g., 4, 5, 6, etc.). In some embodiments, TCN blocks can be associated with varying kernel sizes. For example, TCN blocks that appear earlier in the temporal convolutional network  300  can be associated with a larger kernel size while TCN blocks that appear later in the temporal convolutional network  300  can be assigned progressively smaller kernel sizes. As an example, earlier TCN blocks can be associated with a kernel size of 4 while later TCN blocks can be associated with a kernel size of 2. Many variations are possible. In some embodiments, TCN blocks can be associated with a fixed number of filters. In some embodiments, the number of filters associated with a TCN block can vary. For example, the number of filters associated with a TCN block can increase at each layer of the temporal convolutional network  300 . As an example, the number of filters can increase at each layer following powers of two. 
     At block  304 , an input layer receives (or processes) an audio signal  302 . For example, the audio signal  302  may correspond to a voice sample (or recording) associated with a human speaker. The input layer can receive audio signals based on a sampling rate (e.g., 16 kHz, 44.1 kHz, etc.), such as uncompressed pulse-code modulation (PCM) audio signals. 
     At block  306 , the audio signal  302  can be passed through (or processed by) a set of trained temporal convolutional filters associated with a first TCN block included in the temporal convolutional network  300 . The first TCN block can generate at least one first output. At block  308 , the at least one first output can be passed through a set of trained temporal convolutional filters associated with a second TCN block included in the temporal convolutional network  300 . The second TCN block can generate at least one second output. At block  310 , the at least one second output can be passed through a set of trained temporal convolutional filters associated with a third TCN block included in the temporal convolutional network  300 . The third TCN block can generate at least one third output. At block  312 , the at least one third output can be passed through a set of trained temporal convolutional filters associated with a fourth TCN block included in the temporal convolutional network  300 . The fourth TCN block can generate at least one fourth output. At block  314 , the at least one fourth output can be passed through a set of trained temporal convolutional filters associated with a fifth TCN block included in the temporal convolutional network  300 . The fifth TCN block can generate at least one fifth output. At block  316 , the at least one fifth output can be passed through a set of trained temporal convolutional filters associated with a sixth TCN block included in the temporal convolutional network  300 . The sixth TCN block can generate at least one sixth output. At block  318 , the at least one sixth output can be passed through a set of trained temporal convolutional filters associated with a seventh TCN block included in the temporal convolutional network  300 . The seventh TCN block can generate at least one seventh output. At block  320 , the at least one seventh output can be passed through a set of trained temporal convolutional filters associated with an eighth TCN block included in the temporal convolutional network  300 . The eighth TCN block can generate at least one eighth output. 
     At block  322 , the at least one eighth output can be passed through an identity embedding layer included in the temporal convolutional network  300 . The identity embedding layer can generate an identity embedding (or vector) that represents the audio signal  302 . For example, the identity embedding can be a fixed-size vector that can be generated at some high frequency, such as every 64 milliseconds. At block  324 , the identity embedding can be passed through a normalization layer included in the temporal convolutional network  300 . At block  326 , the identity embedding can be used for various applications. For example, in some embodiments, the identity embedding can be evaluated to identify a speaker. In some embodiments, the identity embedding can be evaluated to distinguish between speakers. For example, the identity embedding can be clustered along with other identity embeddings. In this example, all identity embeddings included in the same cluster can be associated with the same speaker. Many variations are possible. Depending on the implementation, the temporal convolutional network  300  and its TCN-based pre-processing architecture can vary in a number of ways including variations to the number of TCN blocks used, the number of filters associated with a TCN block, the pooling size of a max pooling operation associated with a TCN block, and kernel sizes associated with TCN blocks. For example, in an embodiment, a model with a shorter receptive field and low memory footprint can be built by reducing the kernel size of convolutions (e.g., kernel size 3) and setting a maximum number of filters (e.g., 64 filters). Many variations are possible. 
       FIG.  3 B  illustrates an example first TCN block  350  of a temporal convolutional network, according to an embodiment of the present technology. For example, the first TCN block  350  can be the first TCN block associated with block  306  of  FIG.  3 A . The TCN block  350  can be implemented by the convolutional neural network module  202  of  FIG.  2   . 
     At block  352 , an input layer receives (or processes) an audio signal. For example, the audio signal  302  may correspond to a voice sample (or recording) associated with a human speaker. The input layer can receive audio signals based on some sampling rate (e.g., 16 kHz, 44.1 kHz, etc.), such as uncompressed pulse-code modulation (PCM) audio signals. 
     At block  354 , the audio signal can be passed though (or processed by) a set of trained convolutional filters associated with a first convolutional layer (“Convolution 2”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The first convolutional layer can generate at least one first output. The at least one first output can be passed through an additive skip connection and provided to a max pooling layer at block  368 . 
     At block  356 , the audio signal can be passed through a set of trained convolutional filters associated with a second convolutional layer (“Convolution 0”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The second convolutional layer can generate at least one second output. In various embodiments, the first convolutional layer and the second convolutional layer are implemented as consecutive one-dimensional convolutional layers using causal padding. In such embodiments, the second convolutional layer can be bypassed by the additive skip connection. At block  358 , a first activation function (“Activation 0”) can be performed in relation to the at least one second output generated by the second convolutional layer. The first activation function can generate at least one third output. For example, the first activation function can be a sigmoid or softmax function. At block  360 , the at least one third output can be passed through a first spatial dropout (“Spatial Dropout 0”). The first spatial dropout can generate at least one fourth output. At block  362 , the at least one fourth output can be passed through a set of trained convolutional filters associated with a third convolutional layer (“Convolution 1”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The third convolutional layer can generate at least one fifth output. At block  364 , a second activation function (“Activation 1”) can be performed in relation to the at least one fifth output generated by the third convolutional layer. The second activation function can generate at least one sixth output. For example, the second activation function can be a sigmoid or softmax function. At block  366 , the at least one sixth output can be passed through a second spatial dropout (“Spatial Dropout 1”). The second spatial dropout can generate at least one seventh output. The at least one first output from the first convolution layer at block  354  and the at least one seventh output from the second spatial dropout at block  366  can be passed through the additive skip connection and to a max pooling layer. 
     At block  368 , the max pooling layer can generate an output that is passed to a subsequent TCN block, as described below in reference to  FIG.  3 C . In various embodiments, each TCN block can include a max pooling operation. The max pooling operations can allow the temporal convolutional network to compute over temporally extended swathes of inputted audio data as each layer processes the inputted audio data to generate a signal that is fed into a subsequent layer. The signal can gradually be compressed over the time dimension as the signal is propagated through layers of the temporal convolutional network, which also allows for a gradual increase of the feature dimension. The max pooling operations can also allow for controlling memory consumption by reducing the temporal size of the inputted audio data while enabling customization of a receptive field size associated with the temporal convolutional network. 
       FIG.  3 C  illustrates an example second TCN block  370  of a temporal convolutional network, according to an embodiment of the present technology. For example, the second TCN block  370  can be the second TCN block associated with block  308  of  FIG.  3 A . The TCN block  370  can be implemented by the convolutional neural network module  202  of  FIG.  2   . 
     At block  372 , an output generated by a preceding TCN block can be passed though (or processed by) a set of trained convolutional filters associated with a first convolutional layer (“Convolution 5”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The first convolutional layer can generate at least one first output. The at least one first output can be passed through an additive skip connection and to a max pooling layer at block  386 . 
     At block  374 , the output generated by the preceding TCN block can be passed through a set of trained convolutional filters associated with a second convolutional layer (“Convolution 3”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The second convolutional layer can generate at least one second output. In various embodiments, the first convolutional layer and the second convolutional layer are implemented as consecutive one-dimensional convolutional layers using causal padding. In such embodiments, the second convolutional layer can be bypassed by the additive skip connection. At block  376 , a first activation function (“Activation 2”) can be performed in relation to the at least one second output generated by the second convolutional layer. The first activation function can generate at least one third output. For example, the first activation function can be a sigmoid or softmax function. At block  378 , the at least one third output can be passed through a first spatial dropout (“Spatial Dropout 2”). The first spatial dropout can generate at least one fourth output. At block  380 , the at least one fourth output can be passed through a set of trained convolutional filters associated with a third convolutional layer (“Convolution 4”). For example, the set of trained convolutional filters can perform one-dimensional (1D) convolutions. The third convolutional layer can generate at least one fifth output. At block  382 , a second activation function (“Activation 3”) can be performed in relation to the at least one fifth output generated by the third convolutional layer. The second activation function can generate at least one sixth output. For example, the second activation function can be a sigmoid or softmax function. At block  384 , the at least one sixth output can be passed through a second spatial dropout (“Spatial Dropout 3”). The second spatial dropout can generate at least one seventh output. The at least one first output from the first convolution layer at block  372  and the at least one seventh output from the second spatial dropout at block  384  can be passed through the additive skip connection and to the max pooling layer. At block  386 , the max pooling layer can generate an output that is passed to a subsequent TCN block, such as the third TCN block associated with block  310  of  FIG.  3 A . 
       FIG.  4 A  illustrates an example identity space  400 . For example,  FIG.  4 A  illustrates identity embedding mappings  402  of voice samples associated with different speakers in the identity space  400 . In this example, the mappings  402  are determined by an untrained temporal convolutional network. As a result, the mappings  402  are indistinguishable from one another. In contrast,  FIG.  4 B  illustrates another example identity space  450 , according to an embodiment of the present technology. In  FIG.  4 B , identity embeddings of voice samples are mapped to the identity space  450  by a temporal convolutional network that has been trained as described herein. In this example, the trained temporal convolutional network has learned to map identity embeddings so that mappings of voice samples associated with the same speaker appear closer together in the identity space  450  than mappings of voice samples associated with different speakers. For example,  FIG.  4 B  shows clusters of mappings including a cluster  452  associated with a Speaker A, a cluster  454  associated with a Speaker B, and a cluster  456  associated with a Speaker C. In various embodiments, the clusters can be labeled and used for classification. For example, in some embodiments, the clusters can be used to verify a speaker, for example, for biometric applications. For example, for any previously enrolled (or known) speaker, a determination can be made if an identity vector associated with a new voice sample corresponds to a cluster associated with the previously enrolled speaker. In some embodiments, clusters can be used to identify a speaker. For example, a speaker associated with a voice sample can be determined by mapping an identity embedding of the voice sample to a cluster of identity embeddings associated with the speaker. In some embodiments, clusters of voice samples can be used for speaker diarization. For example, given a conversation containing a set of voices, sometimes overlapping, segments of audio that belong to each speaker can be determined. If some of the speakers have previously been enrolled (e.g., identified, labeled, etc.), this can be combined with speaker verification to determine known identities in addition to timestamps. Many variations are possible. 
     Hardware Implementation 
     The foregoing processes and features can be implemented by a wide variety of machine and computer system architectures and in a wide variety of network and computing environments.  FIG.  5    illustrates an example machine  500  within which a set of instructions for causing the machine to perform one or more of the embodiments described herein can be executed, in accordance with an embodiment of the present technology. The embodiments can relate to one or more systems, methods, or computer readable media. The machine may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The computer system  500  includes a processor  502  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory  504 , and a nonvolatile memory  506  (e.g., volatile RAM and non-volatile RAM, respectively), which communicate with each other via a bus  508 . The processor  502  can be implemented in any suitable form, such as a parallel processing system. In some cases, the example machine  500  can correspond to, include, or be included within a computing device or system. For example, in some embodiments, the machine  500  can be a server, desktop computer, a laptop computer, personal digital assistant (PDA), an appliance, a wearable device, a camera, a tablet, or a mobile phone, etc. In one embodiment, the computer system  500  also includes a video display  510 , an alphanumeric input device  512  (e.g., a keyboard), a cursor control device  514  (e.g., a mouse), a drive unit  516 , a signal generation device  518  (e.g., a speaker) and a network interface device  520 . 
     In one embodiment, the video display  510  includes a touch sensitive screen for user input. In one embodiment, the touch sensitive screen is used instead of a keyboard and mouse. The disk drive unit  516  includes a machine-readable medium  522  on which is stored one or more sets of instructions  524  (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions  524  can also reside, completely or at least partially, within the main memory  504  and/or within the processor  502  during execution thereof by the computer system  500 . The instructions  524  can further be transmitted or received over a network  540  via the network interface device  520 . In some embodiments, the machine-readable medium  522  also includes a database  525 . 
     Volatile RAM may be implemented as dynamic RAM (DRAM), which requires power continually in order to refresh or maintain the data in the memory. Non-volatile memory is typically a magnetic hard drive, a magnetic optical drive, an optical drive (e.g., a DVD RAM), or other type of memory system that maintains data even after power is removed from the system. The non-volatile memory  506  may also be a random access memory. The non-volatile memory  506  can be a local device coupled directly to the rest of the components in the computer system  500 . A non-volatile memory that is remote from the system, such as a network storage device coupled to any of the computer systems described herein through a network interface such as a modem or Ethernet interface, can also be used. 
     While the machine-readable medium  522  is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present technology. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. The term “storage module” as used herein may be implemented using a machine-readable medium. 
     In general, routines executed to implement the embodiments of the invention can be implemented as part of an operating system or a specific application, component, program, object, model (e.g., machine learning model), network (e.g., neural network), module or sequence of instructions referred to as “programs” or “applications”. For example, one or more programs or applications can be used to execute any or all of the functionality, techniques, and processes described herein. The programs or applications typically comprise one or more instructions set at various times in various memory and storage devices in the machine and that, when read and executed by one or more processors, cause the computing system  500  to perform operations to execute elements involving the various aspects of the embodiments described herein. 
     The executable routines and data may be stored in various places, including, for example, ROM, volatile RAM, non-volatile memory, and/or cache memory. Portions of these routines and/or data may be stored in any one of these storage devices. Further, the routines and data can be obtained from centralized servers or peer-to-peer networks. Different portions of the routines and data can be obtained from different centralized servers and/or peer-to-peer networks at different times and in different communication sessions, or in a same communication session. The routines and data can be obtained in entirety prior to the execution of the applications. Alternatively, portions of the routines and data can be obtained dynamically, just in time, when needed for execution. Thus, it is not required that the routines and data be on a machine-readable medium in entirety at a particular instance of time. 
     While embodiments have been described fully in the context of computing systems, those skilled in the art will appreciate that the various embodiments are capable of being distributed as a program product in a variety of forms, and that the embodiments described herein apply equally regardless of the particular type of machine- or computer-readable media used to actually effect the distribution. Examples of machine-readable media include, but are not limited to, recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs), etc.), among others, and transmission type media such as digital and analog communication links. 
     Alternatively, or in combination, the embodiments described herein can be implemented using special purpose circuitry, with or without software instructions, such as using Application-Specific Integrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can be implemented using hardwired circuitry without software instructions, or in combination with software instructions. Thus, the techniques are limited neither to any specific combination of hardware circuitry and software, nor to any particular source for the instructions executed by the data processing system. 
     For purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the description. It will be apparent, however, to one skilled in the art that embodiments of the disclosure can be practiced without these specific details. In some instances, modules, structures, processes, features, models, networks, and devices are shown in block diagram form in order to avoid obscuring the description or discussed herein. In other instances, functional block diagrams and flow diagrams are shown to represent data and logic flows. The components of block diagrams and flow diagrams (e.g., modules, engines, blocks, structures, devices, features, etc.) may be variously combined, separated, removed, reordered, and replaced in a manner other than as expressly described and depicted herein. 
     Reference in this specification to “one embodiment”, “an embodiment”, “other embodiments”, “another embodiment”, “in various embodiments,” “for example,” “in another example,” or the like means that a particular feature, design, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of, for example, the phrases “according to an embodiment”, “in one embodiment”, “in an embodiment”, “in various embodiments,” or “in another embodiment” or the like in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, whether or not there is express reference to an “embodiment” or the like, various features are described, which may be variously combined and included in some embodiments but also variously omitted in other embodiments. Similarly, various features are described which may be preferences or requirements for some embodiments but not other embodiments. 
     Although embodiments have been described with reference to specific exemplary embodiments, it will be evident that the various modifications and changes can be made to these embodiments. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than in a restrictive sense. The foregoing specification provides a description with reference to specific exemplary embodiments. It will be evident that various modifications can be made thereto without departing from the broader spirit and scope as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense. 
     Although some of the drawings illustrate a number of operations or method steps in a particular order, steps that are not order dependent may be reordered and other steps may be combined or omitted. While some reordering or other groupings are specifically mentioned, others will be apparent to those of ordinary skill in the art and so do not present an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof. 
     It should also be understood that a variety of changes may be made without departing from the essence of the invention. Such changes are also implicitly included in the description. They still fall within the scope of this invention. It should be understood that this disclosure is intended to yield a patent covering numerous aspects of the invention, both independently and as an overall system, and in both method and apparatus modes. 
     Further, each of the various elements of the invention and claims may also be achieved in a variety of manners. This disclosure should be understood to encompass each such variation, be it a variation of an embodiment of any apparatus embodiment, a method or process embodiment, or even merely a variation of any element of these. 
     Further, the use of the transitional phrase “comprising” is used to maintain the “open-end” claims herein, according to traditional claim interpretation. Thus, unless the context requires otherwise, it should be understood that the term “comprise” or variations such as “comprises” or “comprising”, are intended to imply the inclusion of a stated element or step or group of elements or steps, but not the exclusion of any other element or step or group of elements or steps. Such terms should be interpreted in their most expansive forms so as to afford the applicant the broadest coverage legally permissible in accordance with the following claims. 
     The language used herein has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.