Voice Assist and Control through Hidden Markov Model [HMM] and Natural Language Processing [NLP]

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Voice Assist and Control through Hidden Markov Model [HMM] and Natural Language Processing [NLP]

An Application to Recognize, Interpret and Execute Speech Commands

Mrs. Pooja B S

Assistant Professor: Dept. of Computer Science & Engineering BNM Institute of Technology

Bengaluru, India

Abstract The absence of a system where spoken commands to a computer are standardized, or at least confined to a dialect, leads to increased complexity in providing speech support. To combat this problem, we propose to develop: VAAC – Voice Assist and Control [pronounced /väk/]: an application to recognize, interpret and execute speech commands across other applications and programs. To implement such a system, techniques of Speech Recognition through HMM models and Natural Language Processing: Entity Extraction by Word Matching & Co-reference and Anaphora Resolution is proposed. To enable automated learning, Machine Learning Techniques of Language Model training, Acoustic Model training and Phonetic model generation, which improve speech recognition accuracy are used.

Keywords Hidden Markov Model, Natural Language Processing, Entity Extraction, Anaphora Resolution, Acoustic Model Training, Phonetic Model generation

  1. INTRODUCTION

    Interacting with computers by voice is a complex task involving speech recognition and natural language processing. In many cases, it makes sense to provide support only for a certain set of commands: this is known as command and control. However, such a system to interpret and interact through voice commands would provide great ease of access through command and control.

    Command and Control applications are concerned with providing the user of these systems, the means to control items within their environment with voice commands appropriate to the domain. The appliance of command and control technology may manifest itself in the control of user interface menus in personal computing desktop applications or the control of large scale mechanical or electronic and computing devices. Command and Control to develop a Voice User Interface for programming assistance is implemented. A VUI is the interface to any speech application which relies on technologies like text-to-speech, speech-to-text, Natural Language Processing and Entity Extraction to develop a ubiquitous programming assistant.

    Many command & control programs do exist, for example in Home automation, automobile command and military applications. These are built for very specific applications in platform specific environments, with large amounts of development, and training time. These programs are not

    scalable and maintainable by the general community. Also, they do not provide sufficient accuracy when support for a wider range of commands is necessary.

    Most voice assistants do not provide interfaces into other applications. There is no single program which can take vocal input and execute commands in the format of an input device. There is a lack of a dialect for spoken commands to a computer. We address this problem, and propose VAAC, which fundamentally is the equivalent of an input device which takes speech as input and executes commands as output.

  2. SYSTEM DESIGN

    1. Proposed Model

      In the proposed approach of the implementation of VAAC, it uses PocketSphinx as the speech recognition engine. PocketSphinx is an open source toolkit for speech recognition. The system requires SphinxBase and SphinxTrain libraries for setting up and training the PocketSphinx model. It consists of a framework that eases the creation of a PocketSphinx model for an individual speaker to perform command and control, and provides a simple interface to make use of this model to perform basic tasks in other applications.

      The system on initial deployment can support applications such as: Mozilla Firefox, Visual Studio Code, Gedit, Gnome- Terminal, Nautilus and will work on Ubuntu 18.04 with the Gnome3 shell. It works by matching the words in the command to a list of standard commands supported by the application. These standard commands will then be translated into different types of keyboard events before being sent to the target application via xdotool and other utilities.

      Fig 1. Proposed Model for VAAC

      The system aims at providing sufficient support to complement actions performed using other input devices and enhancing device accessibility by incorporating it in all computing environments. Following are the vital components utilized to implement the VAAC system.

      1. Pocketsphinx: Speech recognition can be useful in many scenarios such as personal assistant bots, dictation, voice command-based control systems, audio transcriptions, quick notes with audio support, voice-based authentication, etc. PocketSphinx is a library that depends on another library called SphinxBase which provides common functionality across all CMUSphinx projects. The CMUSphinx toolkit is a speech recognition toolkit with various tools used to build speech applications. CMUSphinx contains several packages for different tasks and applications.

      2. xdotool: It lets to programmatically (or manually) simulate keyboard input and mouse activity, move, and resize windows, etc. It does this using X11's XTEST extension and other XLib functions. There is some support for Extended Window Manager Hints. Aliases exist for alt, ctrl, shift, super, and meta which all map to Foo_L, such as Alt_L and Control_L, etc. In cases where the keyboard does not actually have the key that want to type, xdotool will automatically find an unused keycode and use that to type the key. Command Chaining allows the consumption of pending arguments or until a new xdotool command is seen, because no xdotool commands are valid keystrokes.

      3. NCurses (New Curses): It is a programming library that provides an application programming interface (API) that allows the programmer to write text-based user interfaces in a terminal-independent manner. It is a toolkit for developing GUI application software that runs under a terminal emulator. It also optimizes screen changes, in order to reduce the latency experienced when using remote shells.

      4. wmctrl: It is a tool or rather a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctrl

      can query the window manager for information, and request for certain window management actions to be taken. wmctrl is controlled entirely by its command line arguments. These command line arguments are used to specify the action to be performed (with options that modify behavior) and any arguments that might be needed to perform these actions. Only one action can be executed with the invocation of the wmctrl command.

      Initially, the user needs to set up a few resources before making use of the text corpus facilitated by the application to make recordings of the corpus. The recordings are made using internal or external microphones. The grammar file of the recordings is generated, and the model is set up to train the VAAC corpus. Use of short phrases is recommended although the phrases need not be exact commands. The recordings try to balance the frequency of words so that each word accounts to equal weights for their accurate identification.

      Notably, the corpus contains a list of commands, specific to the application supported by the system. Once the model is trained, the application is ready for use. It expects vocal inputs which need to be commands within the trained corpus that are then converted to raw text. From the obtaine text, entities like command and the target application are extracted. To ensure the exact matching of the entities, word- matching is used. Efficient algorithms are utilized over sorted command lists for optimum searching to ensure results in real time.

      Once the application starts running, the user has the option to either give vocal inputs or type the commands in the command prompt as a failsafe. The commands are placed in a buffer from which the target application is extracted first. This is proceeded by applying certain filters to the text to classify and search the matching command.

      The obtained entities include type of command, keystroke and the target application which are passed to the executor module that generates the relevant keystrokes to facilitate the execution of the command over the respective target application. Hence the executable command is generated from the entities by matching the keyboard events corresponding to the command entity.

      The application can be left running in the background for the user to use it as an assistant while using the supported applications.

      The user can also type help followed by the command name to get the documentation on the usage of that respective command. To terminate the execution of VAAC in background, the user can use exit command. In case of an ambiguity, the application makes sure not to disturb the flow of the user by not executing any junk vocal input detected by it. The user can also view the commands detected by the application immediately on the terminal after they speak and can also check the log file to evaluate the accuracy.

    2. Data Flow Diagrams

      1. DFD Level 0: The input device, generally the microphone or keyboard provide the initial input to the VAAC system which identifies signals and keystrokes and transmits it to the application module to perform necessary operations.

    Fig. 2. Data Flow Diagram Level 0

    ii DFD Level 1: The process is represented in a higher detail. The input from the microphone and keyboard in the form of voice and text is sent to the terminal, which then converts these signals into textual format and sends it to the extractor module. The extractor module classifies the text into entities such as types of commands, key strokes and target applications. This is then passed through another extractor module where signals and keystrokes entered are identified and further sent to the application module to perform necessary operations.

    Fig. 3. Data Flow Diagram Level 1

    iii DFD Level 2: The inputs in the form of text and voice are directed to a buffer, this module converts the input into textual form which is sent to identify the target application and to a filter module where the test is classified and searched for commands. These commands are passed onto an executor module and finally the signals from this module is channeled to the application module to perform the necessary operations.

  3. IMPLEMENTATION

    This is the logical conclusion, after evaluating, deciding, visioning, planning, applying for funds and finding the financial resources of a project. Technical implementation is one part of executing a project. This section discusses about the various algorithms that are used, and the reason behind using them.

    This section elaborates on the functioning and the operations of the important modules that VAAC is comprised of, the algorithms and approach used in implementation are also defined in this section. Following are the modules implemented –

    1. Recorder Module

      When the recorder module is initiated, the recorder.py script prompts a phrase from the corpus and records it into a file in the recordings folder. If the phrase recorded is a single word, it will be stored under the recordings/{word}/{word}_{n}.wav extension. Else if the phrase recorded is a line from the corpus, it will be stored under the

      recordings/{corpus_name}/{line_number}_{n}.wav extension, where n is the nth recording of the phrase recorded by the module. To initiate recording, the user is expected to read aloud the prompted phrase into an audio input device such as a microphone. Relevant options are displayed to start recording, stop recording, store recording, re-record without storing and to exit. If one makes any mistakes when recording it can be skipped using the re-record while storing option. However, if it is saved by mistake and one wants to redo it, simply delete the recording, and run recorder again. It will automatically search for missing files and prompt the corresponding phrase.

    2. Setup Module

      Fig. 5. Recorder Module

      Fig. 4. Data Flow Diagram Level 2

      The Setup Module has the ability to unzip and extract the contents of TAR****.tgz file downloaded from lmtool into vaac_model/etc extension. The lmtool builds a consistent set of lexical and language model files for decoders. The target decoders are the Sphinx family, though any system that can read ARPA-format files can use them. Currently lmtool is configured for the English language.

      The input to lmtool is a file containing the corpus which has to be trained. Notably, a corpus file needs to be generated consisting of all sentences that needs to be recognized by the decoder. The sentences should be one to a line without any

      punctuations. It also renames the files to have a prefix vaac_model. This is to easily identify and streamline the process.

      Fig. 6. Setup Module

    3. Terminal Module

      The terminal.py script provides an interface to the client to use VAAC. One can type into this terminal, or speak into it. Because of inherent inaccuracies in speech recognition, the terminal will not prompt messages for commands that do not make 'sense'. That means, if VAAC cannot understand a certain command, it will not prompt errors in the terminal. However, such errors are recorded in the logs.

      This module acts as an interface between the target application and the user. It allows the users to speak their commands that are entered into the terminal, to search for commands that available and specific to that application or to list out generic commands that can be executed on any application window.

      The terminal normally accepts simple, natural language commands containing words already recorded by the user during recordings. This means that the quality of recordings plays a significant role in identifying the words. In case the words spoken or detected are not a part of the corpus, similar sounding words from the corpus are referenced.

      Fig. 7. Terminal Module

    4. Extractor Module

    The extractor module comprises of the Extractor class. This Extractor class provides various methods to extract and write words from the commands to run them. There are certain

    filters inside this, these filter methods return the matched command.

    The extractor module assumes the following default parameters:

    1. Browser: Mozilla Firefox.

    2. Text editor: Gedit.

    3. IDE: Visual Studio Code. iv Terminal: Gnome-terminal. v Files: Nautilus.

    The extractor module accepts the input from the terminal, evaluates it and determines the target application. It also detects the command that needs to be performed in that application.

    Fig. 8. Extractor Module

  4. RESULTS AND DISCUSSIONS

    Following are a set of results and corresponding screenshots depicting the functioning of VAAC –

    1. Test 1: To open an application (example used: Firefox) The first screenshot is the terminal view after the speech has been recognized and converted to text. In the terminal program, the converted text is analyzed to realize the target application (here, Firefox) and the action (here, to launch it).

      Fig. 9. Test window to open an application

      The system understands clearly that the application needs to be launched and performs the action as depicted in the screenshot as seen in the next page.

      Fg. 10. Test result of successfully opened application

    2. Test 2: Ignoring unwanted commands and performing no action for the same (example used: VS Code)

      Here, we command VAAC to open the VS Code. VAAC unfortunately for the first time does not identify the word open and instead recognizes undo (a similar sounding word). Although when spoken the second time, it recognizes and opens VS Code. Notably, despite of recognizing the word undo in place of code, VAAC does not disturb the regular flow by performing unexpected actions. Since there is no active application initially when VAAC is run, undo does not make any sense, it is ignored by VAAC.

      Fig. 11. Test window where unwanted command is uttered

      VS Code is opened on the second utterance as depicted in the screenshot below.

      Fig. 12. Test result of VS code being opened ignoring unwanted commands

    3. Test 3: Testing VAAC within an application (example used: Firefoz)

      Initially the user commands Firefox, which is executed, and the application is launched. The user has multiple browser windows open and commands next tab. VAAC understands the context and shifts to the next tab.

      Fig. 13. Test window of VAAC operating within an application

      This is the tab that the user was browsing on initially.

      Fig. 14. Initial tab view of Firefox before execution of command

      This is the screenshot after VAAC executes the next tab

      command and shifts the browser to the next tab.

      Fig. 15. Tab view of Firefox after execution of command

    4. Test 4: Testing VAAC within an application (example used: Firefox and gedit)

      Initially, the user says Firefox. VAAC executes it. The user selects the text in Firefox and give a vocal command copy. Internally, VAAC copies it as shown below.

      Fig. 16. Internal view of image copied by VAAC

      VAAC copies the selected text and ignores the word new which is not relevant in the current context of the application that is running.

      Fig. 17. Window view of VAAC ignoring commands that are irrelevant to the context

      After ignoring new, VAAC recognizes paste but it still does not get the right target application from the user. Although it comes across unnecessary commands, VAAC still stores the contents and until it recognizes the command paste gedit (gedit is the target application) as depicted in the screenshot below.

      Fig. 18. View of VAAC recognizing commands without mentioned target

      On recognizing paste gedit command, it opens gedit and pastes the copied text there as shown in the screenshot below.

      Fig. 19. Successful execution of command when target application is mentioned

  5. PERFORMANCE EVALUATION

    Fuzzy matching is a technique used in computer-assisted translation as a special case of record linkage. It works with matches that may be less than 100% perfect when finding correspondences between segments of a text and entries in a database of previous translations. FuzzyWuzzy is a library of Python which is used for string matching. Fuzzy string matching is the process of finding strings that match a given pattern. It uses Levenshtein Distance to calculate the differences between sequences.

    Binary search is another algorithm, also known as a half- interval search, is used for computer science to locate a specified value (key) within an array. For the search to be binary, the array must be sorted in either ascending or descending order. Binary search is much faster when compared to Fuzzy algorithm and hence contributes to the improvement in performance of VAAC during runtime where the command needs to be searched from the command lists and various filters applied once the raw text is obtained from the terminal.

    As evident from the screenshot below, for a constant number of commands, the binary search turns out to be a thousand times faster than the Fuzzy search. Notably, both are applied on a sorted data since Binary search can be implemented on a sorted data only. This is another reason why Binary search is faster in this scenario.

    Fig. 20. Comparison of access time between Binary Search and Fuzzy Search

    TABLE I: EVALUATION OF COMMAND RECOGNITION AGAINST THE NUMBER OF RECORDINGS

  6. CONCLUSION

The core intent of VAAC was to develop a Voice User Interface-based approach for a command & control model that accepts user input in the form of speech and translates it to an executable kernel level instruction to provide improved convenience to the user in terms of device usage, across various applications. In the course of development of the project we have gained valuable insights into the software development cycle, concepts of speech analysis, entity mapping and some concepts of natural language processing.

REFERENCES

    1. W Walker, P Lamere, P Kwok, B Raj,R Singh, E Gouvea, P Wolf, J Woelfel, Sphinx-4: A Flexible Open Source Framework for Speech Recognition – CMU Sphinx-4 Speech Recognition System, Technical Report, 2004.

    2. J R. Evans, W A. Tjoland and L G. Allred, Achieving a Hands-Free Computer Interface using Voice Recognition and Speech Synthesis IEEE AES Systems Magazine, 2000.

    3. P Warden, "Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition" – ArXiv, Cornell University, 2018

    4. S Amann, S Proksch ,S Nadi, "FeedBaG: An Interaction Tracker for Visual Studio" – IEEE 24th International Conference on Program Comprehension (ICPC), 2016.

    5. T Bulmer, L Montgomery, D Damian, Predicting Developers IDE Commands with Machine Learning – MSR 2018 Association for Computing Machinery, 2018.

    6. H Lee, Y Peirsman, A Chang, N Chambers, M Surdeanu, D Jurafsky, Stanford University,Multi-Pass Sieve Coreference Resolution System, Stanford University CoNLL Shared Task, 2011.

    7. K Lunuwilage, S Abeysekara, L Witharama, S Mendis and S Thelijjagoda, "Web Based Programming Tool with Speech Recognition for Visually Impaired Users- 11th International Conference on Software, Knowledge, Information Management and Applications, 2011

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