ppt for speech recognition

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Result for ' speech recognition '

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Orange speech Bubble - Free Template Design

Smart and innovative presentation slides Drag & drop image placeholders All images included Aspect ratio - 4:3 (normal)

Big Turquois, Circle speech Bubble - Free Presentation Templates

No animation template Trend template Modern, attractive, and business-friendly colors Easy color change

Employee recognition Award Product Deck

Quick and easy to customize Creative slides Professional and unique slides Beautiful presentation decks and templates Creatively crafted slides

Effective speech Outline PPT Presentation

Scalable vectorial PowerPoint shapes and PowerPoint icons Completely editable presentation template Modern business plan Perfect themes for business or educational. Color, size, shading etc. can be modified

Splendid speech Simple PowerPoint Template Design

Easy customization Built-in custom color palette Premium & modern multipurpose Free font used Premade color variation

Human Silhouette with 4 speech Bubble Blocks Diagram

Easy to edit and customize 100% fully editable PowerPoint slides Shapes: fully editable vector graphics 16:9, 4:3 aspect ratios

speech Bubble Infographics Diagram

Easy to edit and customize Quick and easy to customize Easy color change Dark & light backgrounds 16:9, 4:3 aspect ratios

People and speech bubble Infographic Diagram

Standard (4x3) version of this template also available. Easy to edit and customize 100% fully editable PowerPoint slides Quick and easy to customize Dark and light versions of each

4 Vertical List Diagram ( speech Bubble)

Standard (4x3) version of this template also available. Easy to edit and customize 100% fully editable PowerPoint slides Shapes: fully editable vector graphics

Thumbs up with 3 speech Bubble Diagram

Easy customization Easy color change Dark & light backgrounds

3 speech Bubble Infographic Diagram

Basic Matrix Diagram ( speech Bubble)

Easy customization Dark & light backgrounds

speech Bubbles Infographic Diagram

Standard (4x3) version of this template also available. Easy to change colors Dark and light versions of each

speech Bubble at Meeting Diagram

Shapes: fully editable vector graphics Dark & light backgrounds 2 aspect ratios (4:3 & 16:9)

Radial Icon List Diagram ( speech )

Quick and easy to customize Easy color change Dark and light versions of each Aspect ratio - 4:3 (normal), 16:9 (widescreen)

3 Step speech Box Diagram

Easy to edit in PowerPoint Easy to change colors Dark & light backgrounds

Crane with speech Bubble Diagram (Construction)

Easy color change Dark & light backgrounds Aspect ratio - 4:3 (normal), 16:9 (widescreen)

speech Bubble Puzzle Diagram

100% fully editable PowerPoint slides Shapes: fully editable vector graphics Dark & light backgrounds

Radial List with speech Diagram

Standard (4x3) version of this template also available. Easy to edit in PowerPoint Shapes: fully editable vector graphics

Iris recognition Puzzle Diagram (Cyber security)

Standard (4x3) version of this template also available. Quick and easy to customize Easy to change colors Dark & light backgrounds

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Topic and Readings

Overview of Course, Intro to Probability Theory, and ASR Background: N-gram Language Modeling

TTS: Background (part of speech tagging, machine learning, classification, NLP) and Text Normalization

• Read this only if you haven't had phonetics: J+M New Chapter 7: Phonetics
• Note that there is a lot of reading for today.
• J+M New Chapter 8 Speech Synthesis, pages 1-10
• J+M New Chapter 5 Word Classes and Part-of-Speech Tagging, pages 1-36, but skip section 5.6
• These notes on learning decision trees, up to the section called "Assessing the Performance"
• Optional Advanced Reading: Chapter 4 "Text Segmentation and Organisation" from from Taylor, Paul. 2007. Text-to-Speech Synthesis
• Optional Advanced Reading: Chapter 5 "Text Decoding" from Taylor, Paul. 2007. Text-to-Speech Synthesis

TTS: Grapheme-to-phoneme, Prosody (Intonation, Boundaries, and Duration) and the Festival software

• Chapter 8 "Pronunciation" from Taylor, Paul. 2007. Text-to-Speech Synthesis -->
• Read sections 1, 2, 3, 4, 5, and 6.1 and 6.1.1 from Alan Black's lecture notes on TTS and Festival.
• You should be looking at the Festival manual . You don't have to read the whole thing through, but you should skim it so you know where things are in the manual.
• For those who don't know Scheme, (used as Festival's scripting language): Introduction to Scheme for C Programmers, from Cal Tech .

TTS: Waveform Synthesis (Diphone and Unit Selection Synthesis)

• J+M New Chapter 8, pages 25-end
• Optional Advanced Reading: Chapter 16 "Unit Selection Synthesis" from Taylor, Paul. 2007. Text-to-Speech Synthesis
• Optional Advanced Reading: Optional: Section 7 from Alan Black's lecture notes on TTS and Festival.
• Optional Advanced Reading: The rest of Section 6 of Alan Black's lecture notes

ASR: Noisy Channel Model, Bayes, HMMs, Forward, Viterbi

• J+M New Chapter 6: Hidden Markov Models, pages 1-20
• J+M New Chapter 9: Automatic Speech Recognition, pages 1-12

ASR: Feature Extraction and Acoustic Modeling, Evaluation

• J+M New Chapter 10: Speech Recognition: Advanced Topics pages 11-16

ASR: Learning (Baum-Welch) and Disfluencies

• J+M New Chapter 10: Speech Recognition: Advanced Topics pages 1-11

Browse Course Material

Course info, instructors.

• Dr. James Glass
• Prof. Victor Zue

Departments

• Electrical Engineering and Computer Science

As Taught In

• Signal Processing
• Linguistics

Learning Resource Types

Automatic speech recognition, lecture notes.

A full set of lecture slides is listed below, including guest lectures. Lectures 3, 4, and 6 have audio links to speech samples presented during the lectures.

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Speech Recognition

(4 Editable Slides)

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We have introduced the graphic-rich Speech Recognition PPT template to help you add meaning to your text and give a creative makeover to your boring presentations. IT professionals, engineers, and innovation experts can leverage the well-researched deck to portray how the voice recognition enabled program helps users process human speech into text. You can also capitalize on the set to showcase the common usage and applications of voice recognition across the industries, key features of effective speech recognition, speech recognition algorithms, and much more. The striking slides are infused with eye-pleasing illustrations, compelling icons, and modern and sleek designs. 

A Look at the PowerPoint Slides’ Design

• The automation speech recognition system is showcased through a microphone infographic.
• You can display how speech recognition works via a flowchart illustration.
• The speech recognition models and their processes are illustrated in the other slide.
• A well-designed infographic exhibits the steps of processing and transcribing.

Best-in-Class Features

• The users won’t confront restrictions or challenges while downloading, using, or editing the deck according to their objectives.
• You can reuse the set an end number of times by downloading it only once.
• The vector-based graphics offer uninterrupted resolution on all screen dimensions.

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How to use Dictation

Sign in to your Microsoft account using Edge, Firefox, or Chrome.

Wait for the button to turn on and start listening.

Move your cursor to a placeholder or to the slide notes and start speaking to see text appear.

Insert punctuation at any time by saying them explicitly.

Fix mistakes with your keyboard without having to toggle the mic icon off.

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Tip:  * To resume dictation, use the keyboard shortcut ALT + ' or press the Mic icon in the floating dictation menu.

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We are actively working to improve these languages and add more locales and languages.

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* Preview Languages may have lower accuracy or limited punctuation support.

This service does not store your audio data or transcribed text.

Your speech utterances will be sent to Microsoft and used only to provide you with text results.

For more information about experiences that analyze your content, see Connected Experiences in Office .

Troubleshooting

Can't find the dictate button

If you can't see the button to start dictation:

check if you are on the new Edge, Firefox, or Chrome.

make sure you're signed in with your Microsoft account.

on Windows: make sure you have Windows 10 or above.

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If you see the dictate button is grayed out:

make sure the document is not in a Read-Only state.

Microphone doesn't have access

If you see "Please check your audio settings" or "We don’t have access to your microphone":

make sure no other application or web page is using the microphone and try again.

Microphone isn't working

If you see "There is a problem with your microphone" or "We can’t detect your microphone":

make sure the microphone is plugged in.

test the microphone to make sure it's working.

on Windows 10: check the microphone settings in Control Panel.

on Windows 10: also see How to set up and test microphones in Windows 10.

on a Surface running Windows 10: Adjust microphone settings.

Dictation can't hear you

If you see "Dictation can't hear you" or if nothing appears on the screen as you dictate:

make sure your microphone is not muted.

adjust the input level of your microphone.

move to a quieter location.

if using a built-in mic, consider trying again with a headset or external mic.

Accuracy issues or missed words

If you see a lot of incorrect words being output or missed words:

make sure you're on a fast and reliable internet connection.

avoid or eliminate background noise that may interfere with your voice.

try speaking more deliberately.

check to see if the microphone you are using needs to be upgraded.

Move your cursor to a placeholder to the slide notes and start speaking to see text appear.

Fix mistakes with keyboard without having to toggle the mic icon off.

Tip:  You can also move your cursor to a new comment to dictate feedback for others.

Dictate is not available in Office 2016 or 2019 without Microsoft 365 .

make sure you have Windows 10 or above.

Make sure the document is not in a Read-Only state.

If you see "We don’t have access to your microphone":

check the microphone settings in Control Panel.

also see How to set up and test microphones in Windows 10

on a Surface running Windows 10: Adjust microphone settings

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Artificial Intelligence for Speech Recognition

Published by Rudolf Wiggins Modified over 6 years ago

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Presentation on theme: "Artificial Intelligence for Speech Recognition"— Presentation transcript:

Pseudo-Relevance Feedback For Multimedia Retrieval By Rong Yan, Alexander G. and Rong Jin Mwangi S. Kariuki

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Restaurant AI Voice

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Table of contents, the rise of voice ai in restaurants, enhancing the drive-thru experience, revolutionizing phone orders, the power of ai in online and mobile ordering, integrating ai with kiosks and pos systems, the benefits of voice ai for restaurants, real-world use cases, the future of voice ai in the restaurant industry, speechify text to speech api.

In today's fast-paced world, the restaurant industry is constantly evolving to meet customer demands and improve operational efficiency. One of the most exciting developments is the integration of voice AI technology into various aspects of restaurant operations. As someone who loves exploring innovative solutions, I've been fascinated by how artificial intelligence is transforming the way we order food. From drive-thru lanes to phone orders, voice AI is enhancing the entire ordering experience.

Imagine pulling up to a drive-thru and being greeted by a friendly, efficient voice assistant that takes your order without any hiccups. This is no longer a futuristic dream but a present-day reality. Companies like SoundHound and Conversenow are pioneering voice AI solutions that streamline the ordering process, reduce wait times, and improve order accuracy. By leveraging natural language processing and speech recognition technologies, these systems understand and process customer requests in real-time.

The drive-thru is a critical touchpoint for many fast food and restaurant chains. Traditionally, it's an area prone to order-taking errors and bottlenecks during peak hours. With voice AI, these challenges are being addressed head-on. The voicebot can handle multiple drive-thru orders simultaneously, ensuring that customers receive their food quickly and accurately. This not only boosts customer satisfaction but also allows team members to focus on more complex tasks, thereby enhancing overall restaurant operations.

Phone orders have been a staple of the restaurant industry for decades. However, with voice AI technology, phone systems are now smarter and more efficient. AI-powered phone calls can handle high volumes of phone orders, reducing the strain on staff and minimizing labor costs. This is particularly beneficial during labor shortages, as it allows restaurants to maintain a high level of service without overburdening their employees.

Online ordering and mobile apps have become increasingly popular, and voice AI is making these platforms even more user-friendly. Imagine using your favorite food ordering app and simply speaking your order into your phone. The AI system, with its advanced speech recognition capabilities, understands your request and processes it seamlessly. This voice ordering functionality not only makes the process more convenient for customers but also increases the likelihood of upsell opportunities by suggesting additional menu items.

Kiosks equipped with voice AI are becoming a common sight in many restaurant chains. These systems allow customers to place orders using voice commands, providing a more interactive and efficient ordering experience. Additionally, integrating AI with POS systems ensures that orders are processed accurately and swiftly, further enhancing the guest experience.

The advantages of integrating voice AI into restaurant operations are manifold. Here are some key benefits:

• Streamlined Operations: By automating the order-taking process, voice AI reduces wait times and improves operational efficiency.
• Cost Savings: With AI handling routine tasks, restaurants can save on labor costs and better allocate their human resources.
• Enhanced Customer Experience: Voice AI provides a smoother, more intuitive ordering process, leading to higher customer satisfaction.
• Increased Order Accuracy: AI systems reduce the risk of human error, ensuring that customers receive exactly what they ordered.
• Upsell Opportunities: Voice AI can suggest additional menu items, helping to increase the average order value.
• Adaptability: These systems can be easily integrated with existing POS systems and mobile apps, making them a versatile solution for any restaurant.

Several restaurant chains have already embraced voice AI technology with impressive results. For instance, a major fast-food chain implemented an AI-powered drive-thru system and saw a significant reduction in wait times and an increase in order accuracy. Another chain integrated voice AI into their phone system, leading to a more efficient handling of phone orders and improved customer experience.

As voice AI technology continues to evolve, we can expect even more innovative applications in the restaurant industry. From AI platforms that manage entire ordering systems to generative AI that personalizes the ordering experience for each customer, the possibilities are endless. For franchises and independent restaurants alike, embracing voice AI is not just a trend but a strategic move towards future-proofing their operations.

In conclusion, voice AI is revolutionizing the way we order food, making the process faster, more accurate, and more enjoyable. Whether it's through drive-thru orders, phone calls, or mobile apps, voice AI is set to become an integral part of the restaurant industry, enhancing both customer satisfaction and operational efficiency. So, the next time you place an order, whether it's at a drive-thru or over the phone, don't be surprised if a friendly AI voice greets you – it's just the beginning of a new era in restaurant operations.

The Speechify Text to Speech API is a powerful tool designed to convert written text into spoken words, enhancing accessibility and user experience across various applications. It leverages advanced speech synthesis technology to deliver natural-sounding voices in multiple languages, making it an ideal solution for developers looking to implement audio reading features in apps, websites, and e-learning platforms.

With its easy-to-use API, Speechify enables seamless integration and customization, allowing for a wide range of applications from reading aids for the visually impaired to interactive voice response systems.

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Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

Speech Recognition

Aug 17, 2014

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Speech Recognition. Mital Gandhi Brian Romanowski. Objective - Speech Recognition. Isolated Word Recognition Portable and Fast. System Block Diagram. Recognition – Conceptually. Data Acquisition Training Hidden Markov Models for word set Recognition & Analysis.

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• maximum likelihood
• system block diagram
• public domain fft

Presentation Transcript

Speech Recognition Mital Gandhi Brian Romanowski

Objective - Speech Recognition • Isolated Word Recognition • Portable and Fast

System Block Diagram

Recognition – Conceptually • Data Acquisition • Training Hidden Markov Models for word set • Recognition & Analysis

Theory – Hidden Markov Models • Used to model semi-stationary random processes, like speech • Example: • cat = / k a t /

Viterbi-based Recognition • Calculates the log-maximum likelihood of a series of observations given a particular HMM. • “Which model did this set of data most likely come from?” • Saves time by calculating only a subset of possible paths through the HMM network. • At each new frame, only the most likely transition/observation state pairs are used. • Concepts similar to Dynamic Time Warping

System Components I Volume Box • Sound Input • Amplifier • Reference Voltage • Resistor network (Voltage Dividers) • Voltage followers • Comparator • Microphone voltage vs. Reference • Output • LED bargraph

System Components II Hidden Markov Modeling ToolKit • Data Acquisition • Data Preparation • Parameter Enhancements • Recognition & Analysis

System Components II (cont.) HTK: Data Acquisition & Preparation • Data Acquisition • Recording using HSLab • Live audio input using HVite • Data Preparation • External files: dictionary, config, word lists • Initialization of prototype models (HCompV)

System Components II (cont.) HTK: Sample External Files • Config • Prototype Model

System Components II (cont.) HTK: Training & Recognition • HERest – parameter re-estimation and enhancement tool • Uses information from the energy, delta, & acceleration features in the cepstral domain • HVite for Recognition • Recognition of pre-recorded files or live audio input • A host of external files to support the recognition • Analysis tool HResults to compute accuracy & correctness results

System Components II (cont.) HTK: Results & Analysis • HResults • Computes % values for recognition accuracy and correctness • Results Analysis • NREF = percentage of reference labels correctly recognized • Correction does not penalize for insertion errors

System Components II (cont.) HTK: Preliminary Results ====================== HTK Results Analysis ====================== Date: Mon Sep 30 16:50:59 2002 Ref : 4word_word.mlf Rec : recout.mlf ------------------------ Overall Results -------------------------- SENT: %Correct=25.00 [H=1, S=3, N=4] WORD: %Corr=25.00, Acc=25.00 [H=9, D=0, S=3, I=0, N=12] ======================

System Components II (cont.) HTK: Techniques, Solutions • Input File Specifications • Config • Cepstral mean subtraction, energy enormalization • Prototype model • Number of states per word model • “Optimality” in transition probability assignments (matrix) • Data • “Noise-free” data • As many tokens/samples of each word for training

DSP – System Overview • Initialization • Threshold/Recording • MFCC • Viterbi • Output

DSP - Matlab • Prototype of all important algorithms • Pre-calculated data • Run-time altering of data (debugging) • Downloading and visualization of data • MFCCs

DSP – Recording/Thresholding • Speech Input • Process • Poll A/D for input data (TI-provided code used) • Take only one channel as input • Downsample • Save samples only when signal threshold has been crossed • Lead buffer • Tail buffer • PROBLEMS • Sample transfer modes, single channel selection, threshold values, external microphones • TESTING • Visual and audio inspection in Matlab

DSP – MFCC calculation (1) • Thank You to Takuya Ooura for his Public Domain FFT code. • MFCCs provide an uncorrelated and small set of observation vectors for the HMMs • Process: • Remove DC gain • Pre-emphasize • Hamming window • FFT magnitude • Mel-filter bank • DCT • Lifter

DSP – MFCC calculation (2) • PROBLEMS: • An incorrectly coded pre-emphasis filter • TESTING: • Graphically compared DSP generated MFCCs to: • Matlab MFCCs -> DSP numerical issues • HTK MFCCs -> reference implementation

DSP – Viterbi/Recognition • Uses HTK derived HMMs whose data is contained in a Matlab-generated #include file • PROBLEMS • Numerical concerns • Errors in deriving and coding the formulas.

Final Component Results I: HTK • Pre-recorded Files: ====================== HTK Results Analysis ====================== Date: Mon Dec 02 11:37:46 2002 Ref : testwords.mlf Rec : testwordsoutput.mlf ------------------------ Overall Results -------------------------- SENT: %Correct=94.85 [H=92, S=5, N=97] WORD: %Corr=98.28, Acc=98.28 [H=286, D=0, S=5, I=0, N=291] ====================== • Live Audio Input: ~ 83% • DSP MFCC Files: ~ 65 %

Final Component Results II: DSP • 95% recognition accuracy over 90 trials • 4 words • Trained speaker • Speaker Independence • Indication of some recognition for non-modeled speakers, but not much • Speech => Decision takes approximately 0.88 seconds

Challenges • Speed • Complex project • System integration • Microphone input • Volume Box • HTK • MATLAB & DSP

Recommendations • HTK and DSP • Larger training corpus • Multiple Gaussian mixtures • Channel independence • Continuous Recognition • Real-time MFCC transmission from DSP to HTK • DSP • Code style-fixes • Better user interface

Thank You • Dan Block – For use of his lab and equipment

DSP – MFCC calculation • Thank You to Takuya Ooura for his Public Domain FFT code. • MFCC’s provide an uncorrelated and small set of observation vectors for the HMM’s • Process: • Remove DC gain • Pre-emphasize • Hamming window • FFT magnitude • Mel-filter bank • DCT • Lifter • PROBLEMS: • An incorrectly coded pre-emphasis filter • TESTING: • Graphically compared DSP generated MFCC’s to: • Matlab MFCC’s -> DSP numerical issues • HTK MFCC’s -> reference implementation

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SPEECH RECOGNITION:

SPEECH RECOGNITION:. An Overview of Statistical Modeling of Acoustics. Joseph Picone, PhD Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering. Abstract and Biography.

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Speech Recognition. Acoustic Theory of Speech Production. Acoustic Theory of Speech Production. Overview Sound sources Vocal tract transfer function Wave equations Sound propagation in a uniform acoustic tube Representing the vocal tract with simple acoustic tubes

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Speech Recognition. Pattern Classification 2. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Semi-Parametric Classifiers. Mixture densities Maximum Likelihood (ML) parameter estimation

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Speech Recognition. Hidden Markov Models for Speech Recognition. Outline. Introduction Information Theoretic Approach to Automatic Speech Recognition Problem formulation Discrete Markov Processes Forward-Backward algorithm Viterbi search Baum-Welch parameter estimation

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Speech Recognition. SR has long been a goal of AI If the computer is an electronic brain equivalent to a human, we should be able to communicate with that brain It would be very convenient to communicate by natural language means SR/Voice input Natural language understanding

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Speech Recognition. Dynamic Time Warping & Search. Dynamic Time Warping & Search. Dynamic time warping Search Graph search algorithms Dynamic programming algorithms. Pattern Similarity. Decision Rule. Feature Measurement. Word Reference Templates. Word-Based Template Matching.

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Speech Recognition. An Overview General Architecture Speech Production Speech Perception. Words. Speech Recognition. “How are you?”. Speech Signal. Speech Recognition. Goal: Automatically extract the string of words spoken from the speech signal. Words. Speech Recognition.

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