Patent Description:
Embodiments of the present disclosure relate to speech synthesis, and more specifically, to a method and a device executing a Tacotron system.

Recently, Tacotron-based end-to-end speech synthesis systems have shown impressive text-to-speech (TTS) results from the perspective of naturalness as well as the prosody of the synthesized speech. However, such systems have significant drawbacks in terms of some words in the input text being skipped or repeated while synthesizing speech. This problem is caused by its end-to-end nature where a non-controllable attention mechanism is used for speech generation. The present disclosure addresses these issues by replacing the end-to-end attention mechanism inside the Tacotron system with a duration informed attention network. The proposed network of the present disclosure achieves comparable or improved synthesis performance and addresses the issues within the Tacotron system.

<CIT> discloses a method for converting text to speech. The text is decomposed into a sequence of phonemes and a text feature matrix constructed to define the manner in which the phonemes are pronounced and accented. A spectrum generator then queries a neural network to produce normalized spectrograms based on the input of the sequence of phonemes and features. Normalized spectrograms are fixed-length spectrograms with uniform temporal length (i.e., data size), which enables them to be effectively encoded into a neural network representation. A duration generator output a plurality of durations that are associated with phonemes. A speech synthesizer modifies the temporal length (i.e., de-normalizes) of each normalized spectrogram based on the associated duration, and then combines the plurality of modified spectrograms into speech.

<CIT> discloses a method for automatic speech segmentation into phoneme-like units for use in speech processing applications, and based on segmentation into Broad Phonetic Classes, Sequence-Constrained Vector Quantization, and Hidden-Markov-Models.

<CIT> discloses techniques for neural based speech synthesis with an improved multi-speaker model applicable to Tacotron systems to extend it for learning a plurality of different voices.

The claimed invention provides a method and a device executing a Tacotron system.

In accordance with the invention, a method is provided in claim <NUM>.

In accordance with the invention, a device executing a Tacotron system is provided in claim <NUM>. In accordance with the invention, a non-transitory computer-readable medium storing instructions is provided in claim <NUM>.

TTS systems have diverse applications. However, largely-adopted commercial systems are mostly based on parametric systems which have a large gap as compared to natural human speech. Tacotron is a TTS-synthesis system that is significantly different from conventional parametric-based TTS systems, and is capable of producing highly natural speech sentences. The entire system can be trained in an end-to-end fashion, and replaces a conventional complicated linguistic feature extraction part with an encoder-convolution-bank-highway network-bidirectional-gated-recurrent unit (CBHG) module.

The duration model which has been used in conventional parametric systems is replaced with end-to-end attention mechanism where the alignment between input text (or phoneme sequences) and speech signals are learned from an attention model instead of a Hidden Markov Model (HMM)-based alignment. Another major difference associated with the Tacotron system is that it directly predicts mel/linear spectrum which could be used directly by an advanced vocoder such as Wavenet and WaveRNN for synthesizing high quality speech.

The Tacotron-based systems are capable of generating more accurate and natural-sounding speech. However, Tacotron systems include instabilities such as skipping and/or repeating input texts, which is an inherent drawback when synthesizing speech waveforms.

Some implementations herein address the foregoing input text skipping and repeating problem with Tacotron-based systems while preserving its superior synthesizing quality. Further, some implementations herein address these instability issues and achieve significantly improved naturalness in synthesized speech.

The instability of Tacotron is predominantly caused by its uncontrollable attention mechanism, and there is no guarantee that each input text can be sequentially synthesized without skipping or repeating.

Some implementations herein replace this unstable and uncontrollable attention mechanism with a duration based attention mechanism where the input text is guaranteed to be sequentially synthesized without skipping or repeating. The main reason why attention is needed in Tacotron-based systems is the missing alignment information between source text and a target spectrogram.

Typically, the length of input text is much shorter than that of a generated spectrogram. The single character/phoneme from input text might generate multiple frames of spectrogram while this information is needed for modeling input/output relationships with any neural network architecture.

The Tacotron-based systems have predominantly addressed this problem with an end-to-end mechanism, where the generation of spectrogram relied on a learned attention on source input text. However, such an attention mechanism is fundamentally unstable as its attention is highly incontrollable. Herein, the end-to-end attention mechanism within the Tacotron system is replaced with a duration model that predicts how long a single input character and/or phoneme lasts. In other words, the alignment between an output spectrogram and input text is achieved by replicating each input character and/or phoneme for a predetermined duration. The ground truth duration of input text to learned from our systems are achieved with HMM based forced alignment. With predicted duration, each target frame in spectrogram could be matched with one character/phoneme in the input text. The entire model architecture is plotted in the figure below.

<FIG> is a diagram of an overview of an embodiment described herein. As shown in <FIG>, and by reference number <NUM>, a platform (e.g., a server) receives a text input that includes a sequence of text components. As shown, the text input includes a phrase such as "this is cat. " The text input includes a sequence of text components shown as characters "DH," "IH," "S," "IH," "Z," "AX," "K," "AE," and "AX.

As further shown in <FIG>, and by reference number <NUM>, the platform determines, using a duration model, respective temporal durations of the text components. The duration model includes a model that receives an input text component and determines a temporal duration of the text component. As an example, the phrase "this is a cat" may include an overall temporal duration of one second when audibly output. The respective text components of the phrase may include different temporal durations that, collectively, form the overall temporal duration.

As an example, the word "this" may include a temporal duration of <NUM> milliseconds, the word "is" may include a temporal duration of "<NUM> milliseconds," the word "a" may include temporal duration of <NUM> milliseconds, and the word "cat" may include a temporal duration of <NUM> milliseconds. The duration model determines that respective constituent temporal durations of the text components.

As further shown in <FIG>, and by reference number <NUM>, the platform generates a first set of spectra based on the sequence of text components. For example, the platform inputs the text components into a model that generates output spectra based on input text components. As shown, the first set of spectra includes respective spectra of each text component (e.g., shown as "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," and "<NUM>").

As further shown in <FIG>, and by reference number <NUM>, the platform generates a second set of spectra based on the first set of spectra and the respective temporal durations of the sequence of text components. The platform generates the second set of spectra by replicating the spectra based on the respective temporal durations of the spectra. As an example, the spectra "<NUM>" is replicated such that the second set of spectra includes three spectra components that correspond to the spectra "<NUM>," etc. The platform may use the output of the duration model to determine the manner in which to generate the second set of spectra.

As further shown in <FIG>, and by reference number <NUM>, the platform generates a spectrogram frame based on the second set of spectra. The spectrogram frame is formed by the respective constituent spectra components of the second set of spectra. As shown in <FIG>, the spectrogram frame aligns with a prediction frame. Put another way, the spectrogram frame generated by the platform accurately aligns with an intended audio output of the text input.

The platform, using various techniques, generates an audio waveform based on the spectrogram frame, and provides the audio waveform as an output.

In this way, some implementations herein permit more accurate audio output generation associated with speech-to-text synthesis by utilizing a duration model that determines the respective temporal durations of input text components.

<FIG> is a diagram of an example environment <NUM> in which systems and/or methods, described herein, are implemented. As shown in <FIG>, environment <NUM> may include a user device <NUM>, a platform <NUM>, and a network <NUM>. Devices of environment <NUM> may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

User device <NUM> includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with platform <NUM>. For example, user device <NUM> may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device. In some implementations, user device <NUM> receives information from and/or transmits information to platform <NUM>.

Platform <NUM> includes one or more devices capable of generating an audio waveform using a duration informed attention network for text-to-speech synthesis, as described elsewhere herein. In some implementations, platform <NUM> may include a cloud server or a group of cloud servers. In some implementations, platform <NUM> may be designed to be modular such that certain software components may be swapped in or out depending on a particular need. As such, platform <NUM> may be easily and/or quickly reconfigured for different uses.

In some implementations, as shown, platform <NUM> may be hosted in cloud computing environment <NUM>. Notably, while implementations described herein describe platform <NUM> as being hosted in cloud computing environment <NUM>, in some implementations, platform <NUM> is not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

Cloud computing environment <NUM> includes an environment that hosts platform <NUM>. Cloud computing environment <NUM> may provide computation, software, data access, storage, etc. services that do not require end-user (e.g., user device <NUM>) knowledge of a physical location and configuration of system(s) and/or device(s) that hosts platform <NUM>. As shown, cloud computing environment <NUM> may include a group of computing resources <NUM> (referred to collectively as "computing resources <NUM>" and individually as "computing resource <NUM>").

Computing resource <NUM> includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, computing resource <NUM> may host platform <NUM>. The cloud resources may include compute instances executing in computing resource <NUM>, storage devices provided in computing resource <NUM>, data transfer devices provided by computing resource <NUM>, etc. In some implementations, computing resource <NUM> may communicate with other computing resources <NUM> via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown in <FIG>, computing resource <NUM> includes a group of cloud resources, such as one or more applications ("APPs") <NUM>-<NUM>, one or more virtual machines ("VMs") <NUM>-<NUM>, virtualized storage ("VSs") <NUM>-<NUM>, one or more hypervisors ("HYPs") <NUM>-<NUM>, or the like.

Application <NUM>-<NUM> includes one or more software applications that may be provided to or accessed by user device <NUM> and/or sensor device <NUM>. Application <NUM>-<NUM> may eliminate a need to install and execute the software applications on user device <NUM>. For example, application <NUM>-<NUM> may include software associated with platform <NUM> and/or any other software capable of being provided via cloud computing environment <NUM>. In some implementations, one application <NUM>-<NUM> may send/receive information to/from one or more other applications <NUM>-<NUM>, via virtual machine <NUM>-<NUM>.

Virtual machine <NUM>-<NUM> includes a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. Virtual machine <NUM>-<NUM> may be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by virtual machine <NUM>-<NUM>. A system virtual machine may provide a complete system platform that supports execution of a complete operating system ("OS"). A process virtual machine may execute a single program, and may support a single process. In some implementations, virtual machine <NUM>-<NUM> may execute on behalf of a user (e.g., user device <NUM>), and may manage infrastructure of cloud computing environment <NUM>, such as data management, synchronization, or long-duration data transfers.

Virtualized storage <NUM>-<NUM> includes one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of computing resource <NUM>. In some implementations, within the context of a storage system, types of virtualizations may include block virtualization and file virtualization. Block virtualization may refer to abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without regard to physical storage or heterogeneous structure. The separation may permit administrators of the storage system flexibility in how the administrators manage storage for end users. File virtualization may eliminate dependencies between data accessed at a file level and a location where files are physically stored. This may enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations.

Hypervisor <NUM>-<NUM> may provide hardware virtualization techniques that allow multiple operating systems (e.g., "guest operating systems") to execute concurrently on a host computer, such as computing resource <NUM>. Hypervisor <NUM>-<NUM> may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources.

Network <NUM> includes one or more wired and/or wireless networks. For example, network <NUM> may include a cellular network (e.g., a fifth generation (<NUM>) network, a long-term evolution (LTE) network, a third generation (<NUM>) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks.

<FIG> is a diagram of example components of a device <NUM>. Device <NUM> corresponds to user device <NUM> and/or platform <NUM>. As shown in <FIG>, device <NUM> includes a bus <NUM>, a processor <NUM>, a memory <NUM>, a storage component <NUM>, an input component <NUM>, an output component <NUM>, and a communication interface <NUM>.

Bus <NUM> includes a component that permits communication among the components of device <NUM>. Processor <NUM> is implemented in hardware, firmware, or a combination of hardware and software. Processor <NUM> is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, processor <NUM> includes one or more processors capable of being programmed to perform a function. Memory <NUM> includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by processor <NUM>.

Communication interface <NUM> includes a transceiver-like component (e.g., a transceiver and/or a separate receiver and transmitter) that enables device <NUM> to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface <NUM> may permit device <NUM> to receive information from another device and/or provide information to another device. For example, communication interface <NUM> may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

Device <NUM> may perform one or more processes described herein. Device <NUM> may perform these processes in response to processor <NUM> executing software instructions stored by a non-transitory computer-readable medium, such as memory <NUM> and/or storage component <NUM>. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices.

When executed, software instructions stored in memory <NUM> and/or storage component <NUM> cause/causes processor <NUM> to perform one or more processes described herein.

<FIG> is a flow chart of an example process <NUM> for generating an audio waveform using a duration informed attention network for text-to-speech synthesis. In some implementations, one or more process blocks of <FIG> may be performed by platform <NUM>. In some implementations, one or more process blocks of <FIG> may be performed by another device or a group of devices separate from or including platform <NUM>, such as user device <NUM>.

As shown in <FIG>, process <NUM> includes receiving, by a device, a text input that includes a sequence of text components (block <NUM>).

Platform <NUM> receives a text input that is to be converted to an audio output. The text components includes characters and/or phonemes. The sequence of text components may form a sentence, a phrase, and/or the like.

As further shown in <FIG>, process <NUM> includes determining, by the device and using a duration model, respective temporal durations of the text components (block <NUM>).

The duration model includes a model that receives an input text component, and determines a temporal duration of the input text component. The duration model replaces the end-to-end attention mechanism within a Tacotron system. Platform <NUM> has trained the duration model. For example, platform <NUM> may use machine learning techniques to analyze data (e.g., training data, such as historical data, etc.) and create the duration model. The machine learning techniques may include, for example, supervised and/or unsupervised techniques, such as artificial networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, decision trees, association rule learning, or the like.

The platform <NUM> has trained the duration model by aligning a spectrogram frame of a known duration and a sequence of text components. For example, platform <NUM> may determine a ground truth duration of an input text sequence of text components using HMM-based forced alignment. The platform <NUM> may train the duration model by utilizing prediction or target spectrogram frames of known durations and known input text sequences including text components.

The platform <NUM> inputs the text component into the duration model, and determines information that identifies or is associated with a respective temporal duration of the text component based on an output of the model. The information that identifies or is associated with the respective temporal duration is used to generate the second set of spectra, as described below.

As further shown in <FIG>, process <NUM> includes determining whether a respective temporal duration of each text component has been determined using the duration model (block <NUM>).

The platform <NUM> iteratively, or simultaneously, determines respective temporal durations of the text components. The platform <NUM> determines whether a temporal duration has been determined for each text component of the input text sequence.

As further shown in <FIG>, if respective temporal durations of each text component have not been determined using the duration model (block <NUM> - NO), then process <NUM> may include returning to block <NUM>.

The platform <NUM> inputs text components for which temporal durations have not been determined into the duration model until temporal durations have been determined for every text component.

As further shown in <FIG>, if respective temporal durations of each text component have been determined using the duration model (block <NUM> - YES), then process <NUM> includes generating, by the device, using the Tacotron system, a first set of spectra based on the sequence of text components (block <NUM>).

The platform <NUM> generates output spectra using the Tacotron system that correspond to the text components of the input sequence of text components. The platform <NUM> may utilize a CBHG module to generate the output spectra. The CBHG module may include a bank of <NUM>-D convolutional filters, a set of highway networks, a bidirectional gated recurrent unit (GRU), a recurrent neural network (RNN), and/or other components.

The output spectra may be mel-frequency cepstrsum (MFC) spectra in some implementations. The output spectra may include any type of spectra that is used to generate a spectrogram frame.

As further shown in <FIG>, process <NUM> includes generating, by the device and using the Tacotron system, a second set of spectra based on the first set of spectra and the respective temporal durations of the sequence of text components (block <NUM>).

The platform <NUM> generates the second set of spectra using the first set of spectra and the information that identifies or is associated with the respective temporal durations of the text components.

The platform <NUM> replicates various spectra of the first set of spectra based on the respective temporal durations of the underlying text components that correspond to the spectra. In some cases, the platform <NUM> may replicate a spectra based on a replication factor, a temporal factor, and/or the like. In other words, the output of the duration model may be used to determine a factor by which to replicate a particular spectra, generate additional spectra, and/or the like.

As further shown in <FIG>, process <NUM> includes generating, by the device and using the Tacotron system, a spectrogram frame based on the second set of spectra (block <NUM>).

The platform <NUM> generates a spectrogram frame based on the second set of spectra. Collectively, the second set of spectra forms a spectrogram frame. As mentioned elsewhere herein, the spectrogram frame that is generated using the duration model more accurately resembles a target or prediction frame. In this way, some implementations herein improve accuracy of TTS synthesis, improve naturalness of generated speech, improve prosody of generated speech, and/or the like.

As further shown in <FIG>, process <NUM> includes generating, by the device and using the Tacotron system, an audio waveform based on the spectrogram frame (block <NUM>), and providing, by the device, the audio waveform as an output (block <NUM>).

The platform <NUM> generates an audio waveform based on the spectrogram frame, and provides the audio waveform for output. As examples, the platform <NUM> may provide the audio waveform to an output component (e.g., a speaker, etc.), may provide the audio waveform to another device (e.g., user device <NUM>), and may transmit the audio waveform to a server or another terminal, and/or the like.

Modifications and variations are possible in the scope of the claims.

Claim 1:
A method comprising:
receiving (<NUM>), by a device executing a Tacotron system with an end-to-end attention mechanism, a text input that includes a sequence of characters and/or phonemes; the method is characterised by:
replacing the end-to-end attention mechanism within the Tacotron system with a duration model that predicts how long a single character and/or phoneme lasts;
determining (<NUM>), by the device and using the duration model within the Tacotron system, respective temporal duration of each of the characters and/or phonemes, wherein the duration model is trained using a set of prediction frames of known durations and training characters and/or phonemes;
determining (<NUM>), by the device, whether the respective temporal duration of each of the characters and/or phonemes is determined;
based on determining that the respective temporal duration of each of the characters and/or phonemes is determined, generating (<NUM>), by the device and using the Tacotron system, a first set of spectra based on the sequence of characters and/or phonemes;
based on determining that the respective temporal duration of each of the characters and/or phonemes is determined, generating (<NUM>), by the device and using the Tacotron system, a second set of spectra by replicating respective spectra of the first set of spectra according to the respective temporal durations of the sequence of characters and/or phonemes;
generating (<NUM>), by the device and using the Tacotron system, a spectrogram frame based on the second set of spectra;
generating (<NUM>), by the device and using the Tacotron system, an audio waveform based on the spectrogram frame; and
providing (<NUM>), by the device and using the Tacotron system, the audio waveform as an output.