New Systems and Architectures for Automatic Speech Recognition and Synthesis


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Table of contents

Other objects and advantages of the invention will become apparent as the description proceeds. The present invention is directed to a system with a client-server architecture for Automatic Speech Recognition ASR applications, that comprises:. The DFE dynamically may distribute the process of features extraction between the client and server sides, depending on demand and according to the computational power of the client side and the network bandwidth resources.

The preprocessed acoustic signals may be sent to a WebSockets adaptor being an interface for performing a marshaling operation on the objects of the acoustic signals before being sent to the server side. The web server may include a WebSockets connector being an interface for performing an unmarshaling operation. Feature extraction may be made on the client side, while dynamic feature extraction may be made on the server side.

The adaptation server may use the collected knowledge about the client to search and find the optimal acoustic model for that client and to change the model, if needed. Accordingly, the computational load is adapted to maximize usage of a computational power of the client while reducing the load on server side and minimizing the required data traffic between the client side and the server side, in order to improve performance.

Additional improvement is obtained by starting decoding on the server side as quickly as possible, that is achieved by streaming of chunked packeted feature vectors. Existing message oriented middleware with a universal interface is used to improve scalability, extendibility and adaptivity, as well as using HTTP-based protocols for networking.

The present invention proposes an efficient algorithm of decoding provide maximum scalability and adaptability of the architecture. The proposed Through Feature Streaming Architecture TFSA is multilayered and comprises 5 main layers and additional two layers not shown which are used for clustering.

Each layer has a clearly defined functionality and responsibilities.


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Multilayered Architecture decouples additional services from the application layer the voice processing service , simplifies changes of protocols and components and allows upgrading the TFSA by parts. Service layers are transparent to the speech recognition process.

It means that the process of features extraction may be distributed between the client and server sides in different ways, depending on, the computational power of the client, the network bandwidth, security requirements and etc. This allows optimal distribution of computational loads between the client and the server sides.

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These adaptors implement the same interface as other filters and encapsulate complexities of dedicated transport, marshaling the process of converting the data or the objects into a byte-stream and unmarshaling the reverse process of converting the byte-stream back to their original data or object of feature vectors. The client and the decoder continue to work similar to non-distributed applications, as illustrated in FIG.

The client side also includes VAD in order to separate between speech and non-speech acoustic signals, as well as noise compensation algorithms for environmental noise reduction and echo cancelation. The client side generates feature vectors packets and pipelines them to the server side via a WebSockets a technology that allows performing real-time communication between a client side and a server side, rather than polling for changes back and forth adaptor the adaptor translates between the WebSockets implementation and the actual application.

DFE forms a bi-directional communication channel between a web server and a client that allows messages to be sent back and forth. The header of each chunk of WebSocket in this case has just 2 bytes. Therefore, using such a DFE minimizes network traffic. The amount of transmitted data between the client and server sides is reduced by splitting the feature extraction activity of the FE into two parts.

Static feature extraction is made on the client side, while dynamic feature extraction is made on the server side. When environment compensation or speaker normalization algorithms are used, the client side is responsible to persist accumulating results to improve recognition performance.

Firstly, the live cepstral Mel-Frequency Cepstral Coefficients, or MFCCs mean normalization is improved by storing an initial means vector on the client side between sessions. Secondly, if Vocal Tract Normalization VTN—is a widely used speaker normalization technique which reduces the effect of different lengths of the human vocal tract and results in an improved recognition accuracy of automatic speech recognition systems is used, the warping factor is saved on the client side after detection.

Thirdly, the transformation matrix for reducing dimensions can be stored on the client side, when Linear Discriminate Analysis LDA—a method of pattern recognition and machine learning to find a linear combination of features which separates two or more classes of objects is used. A particular frontend library must be implemented for each client's platform.

Our Solutions

In distributed applications, these layers are essential for creating a scalable solution. The main component of this layer is a Web Server. There are many different commercial and open source Web Servers. In Java based applications, such functions are performed by a Servlet Container, which is the component of a web server that interacts with Java servlets and which is responsible for managing the lifecycle of servlets a Java programming language classes used to extend the capabilities of a server , mapping an URL to a particular servlet and ensuring that the URL requester has the correct access rights.

For example, Jetty is a Java-based open source servlet container that has an embedded support of WebSockets and is used as a transducer that transfers WebSockets' packets to messages.

Panel Questions

The Web Layer is a convenient access point for corporate information systems, such as Lightweight Directory Access Protocol LDAP—standard application protocol for accessing and maintaining distributed directory information services over an IP network or Windows Registry or Database or more specific Radiology Information System RIS—networked software suite for managing medical imagery and associated data.

Using them, the TFSA can have access to additional information about the speaker and domain that maybe useful as for the adaptation acoustic model, as well as for decoding and post-processing for example: gender dependent recognized text correction. Another benefit is that a Web Server provides effective schemes for authentication and authorization of clients, load balancing and fault tolerance the property that enables a system to continue operating properly in the event of the failure of some of its components solutions.

The proposed TFSA also uses Message-Oriented Middleware MOM—is software or hardware infrastructure supporting sending and receiving messages between distributed systems for decoupling the process of feature vector delivery and decoding. Delivered data is buffered in an input queue.


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  • The MOM also provides load balancing and scalability for the server side. It gives a unified and a powerful solution for TFSA subsystems integration, cooperation and supplies reliable transport for organization peer-to-peer connections between the client and the decoding process on the server side.

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    The intermediate layer serves as a shared bus that transmits instructions and data. Thus, in TFSA interoperation between subsystems on server side is message driven. This topology decouples subsystems and allows increasing of overall productivity and fault tolerance, as illustrated in FIG. According to the invention, language modeling process and speaker independent or clustered according to language, accent, gender or other property acoustic models training are performed offline and deployed in time of installation. A Recognition Server RC —the abstraction assigned to encapsulate the process of instantiation of a recognition channel per speaker and organize communication channel between the web layer and the searcher.

    Its instantiation includes fetching from persistent layer speaker independent acoustic model and language model and building global search space. In ASR, the search space the space of all feasible solutions is represented as multilevel embedded graph that includes information from language model that connects possible transition from word to word, where each words is an embedded graph of different variants of its pronunciation and where each phoneme is embedded Hidden Markov Model HMM—is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved hidden states.

    There are two main types of language models: fully connected models and dynamically expanded models. Practically, it is impossible to construct a fully connected model higher than bigram for large vocabulary recognition. Due to the absence of reuse in such an approach, it is needed to construct a different search space for each supported domain, although often overlapping between domains is significant. The proposed TFSA utilizes a dynamic approach, where the search space has static and dynamic parts. The static part named Global search space is shared by all channels.

    For maximal efficiency, the search is stateless and re-enterable to avoid a synchronization problem. Its instantiation is based on building Pronunciation Prefix Tree PPT indexed by acoustic model and by all n-gram a contiguous sequence of n items from a given sequence of text or speech and grammar models that the language model includes. Post processing turns PPT into a closed graph that includes inter-words transitions.

    The dynamic part is built in the process of decoding and is different for each channel. A Searcher or a decoder is the object that encapsulates logic of search algorithm and non-shared data. Generally, direct implementation of the Viterbi algorithm is quite difficult, especially for supporting multiple speaker parallel decoding. The Token Passing algorithm a channel access method where a signal called a token is passed between nodes that authorizes the node to communicate—proposed by Young et al.

    Each token has WLR field. So typically, in Large Vocabulary ASR LVASR decoding algorithm each frame feature vector iterates over tokens that survived in the active list and generates new tokens for all possible transition according to links to dynamic search state that in turn has link to lexical graph and HMM. So it is a double work to expand the search space and to produce tokens. Another problem with tokens is how to manage with multiple transitions from different states into one state to keep Viterbi Approximation which estimates the total likelihood as the probability of a single most likely sequence that takes only transition with maximum score.

    In case of Token Passing it is required to check whether transition exist in active list to compare score and replace, if needed. One of a possible solution for the problem is using hash tables. However, it is memory and time consuming solution. There are solutions which is closely related to specific topology HMM topology and therefor have a loss of generalization.

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    Therefore, the present invention proposes State Passing algorithm that solves above problem and suitable for arbitrarily complex networks affective for n-gram models. By analogy to Token Passing, State Passing traverses over dynamically expanded search space, but the expansion is by search HMM graph entirely. This allows avoiding the Viterbi Approximation problem described above. Instead of producing tokens the search states is used and placed in a Layered Active List.

    Each state implements a SearchState interface that includes the following methods:.

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    Current state in turn invokes the expander to expand itself if was not yet expanded. The target state compares its corresponding field with the counter and detects whether the state already exist in a active lists and simply put itself to active list or update its score. The process much more likely represent spanned search state's life time in an active list—waveform character of Viterbi algorithm. SP is well consistent with Object Oriented Design because it decuples the search algorithm from search space implementation.

    Each certain search state type knows its dedicated active list. Also, states that are not presented in the current active list but existed in the expanded search space will not be considered as garbage by the garbage collector. The Search Space Expander is responsible for expansion a speaker specific search space by its acoustic model. The life circle of the object is identical to the life circle of the Recognition Channel. Acoustic score calculation is one of the most time consuming parts of Viterbi algorithm. Shared States or senone a basic sub-phonetic unit acoustic models significantly reduce it.

    But, as shown in FIG. To avoid multiple scoring caching senone wrappers instead of senones is used. In a case of Bakis a Model Topology according to which as the time increases state index increases or stays the same, from left to right HMM topology expander built HMM search graph very effective. Expander iterates over HMM states from right to left create corresponding HMM search state in array with same index and for the state for all outgoing transition target index create transition to search state that already exist.

    For Ergodic models in which each state in the model can be reached in one step—i. The Layered State Active List consists of 3 level active lists, each of them has different absolute and relative beams:. The Layered State Active List encapsulates algorithm of histogram pruning.

    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis
    New Systems and Architectures for Automatic Speech Recognition and Synthesis New Systems and Architectures for Automatic Speech Recognition and Synthesis

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