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speechify has a text-to-speech (TTS) feature that allows you to read text such as documents, articles, PDFs, and emails. It can also convert books or printed text to audio using optical character recognition technology.You can also use SPEAKIFY to read aloud text taken with a photo.
A boy wanted to read Harry Potter, but every time he couldn't get past the third page and had to leave the library. To help the boy, his father read to him in the afternoons after work. For people with dyslexia, reading a sentence takes the same amount of energy as solving a four-digit division equation by rote. But listening takes much less energy, so the boy was able to comprehend the book.
Speechify was first conceived as a business by dyslexic Cliff Weitzman when he was a college student. He founded speechify based on his own experiences as a child struggling to read and using listening to improve his reading experience. He started speechify so he could read many textbooks, handouts, and PDFs that didn't support audiobooks.
In this article, we'll take a look at the TTS technology being used by YouTubers around the world, and how speechify's TTS course is one of the most advanced in the world.
Why YouTubers use TTS
YouTube creators utilize subtitles and voiceovers to make their videos more readable. Some use their own voice, but many use speech generated by the TTS feature.
YouTubers typically use TTS to add a "synthesized voice"to their videos for the following reasons
● A tool for clear content delivery forYouTubers with accents, dialects, etc.
● Easily insert audio into your videos by minimizing the recording and editing process
● Fewer grammatical errors
● May be easier to listen to for non-native speakers
TTS courses on Speechify
Speechify uses artificial intelligence to process the data you input, then converts and outputs it into the voice of your choice. You can then enter customizations to the output to suit your needs. Below, we'll break down the TTS process in more detail after uploading text, such as a document or webpage, to the Speechify app for the text-to-speech process.
Natural language processing
Speechify uses NLP algorithms to analyze your input text.
Natural language processing (NLP) refers to the field of artificial intelligence concerned with teaching computers how to understand and interpret human language. NLP, specifically for audio transcription or automatic speech recognition, has many applications across industries where humans and technology work together.
Automatic speech recognition utilizes NLP models with the goal of accuracy. Originally, the limits of computers in speech recognition were limited to determining the pitch of a sound, but algorithms are now able to detect patterns in audio samples and determine the meaning of words spoken by a speaker through the sounds of different languages. More recently, deep learning neural networks have been utilized to produce outputs that are even more accurate and require less human supervision.
Speechify's NLP model is also able to identify the meaning and context of words and sentences: it can analyze and understand whether the samen umber means a date or a figure. Ultimately, it aims to understand the punctuation and stable structure of text and be able to accept sentences.
Voice selection and text-to-speech
Once you've turned your text into speech, you can choose from a variety of voices for your output. You can decide based on different accents, languages, or genders.
Once a voice is selected, it begins to convert the input text to speech using a combination of neural networks and rule-based algorithms. Neural networks are trained on large datasets of spoken language so they can accurately reflect the meaning and context of the input text. They also utilize a built-in dictionary to understand correct pronunciation.
Audio processing based on artificial intelligence requires large amounts of high-quality data: custom speech data trained on multiple scenarios.Speech data for machine learning typically includes scenarios such as scripts in the form of responses or spontaneous conversations.
The collected data must be labeled and processed into training data, which involves sampling and digitizing it into a digital audio format.This usually involves segmenting the audio into layers, timestamps, etc. Of course, AI can help with this process as well, but in some cases it can suffer from inaccuracies, especially with audio data, which can be a large task requiring specialized human operators.
If you can't find what you're looking for, or need to create it yourself, you can also work with a data collection/processing partner like Datahunt to create and process data for learning.
DataHunt Success Story - Analyzing Psychometric Data with SpeechTranscription/STT
Output and customization
At the end of the text-to-speech process, you have a spoken version of your input text, which you can play in Speechify's mobile/desktop app or download as an audio file.
You can also adjust the speed or pitch of the output speech, or add commas or emphasis to certain words or phrases. speechify provides customization to TTS output in a variety of ways.
Utilization guides
Speechify's Transcribe feature is a tool that automatically converts audio content into written text. The Transcribe feature is typically used for the following purposes
- Take notes: Recording a lecture, meeting, or interview is an efficient way to capture important information for later review. For example, if you record a lecture and then use Speechify to transcribe it, you'll be able to find key points faster and learn more effectively.
- Accessibility: Transcribe makes audio content accessible to people who are deaf or hard of hearing or who prefer to read rather than listen. For example, videos with audio content can be transcribed using Speechify so that people who are deaf or hard of hearing can follow along.
- Content creation: Transcribe can be a useful tool for content creators who want to repurpose audio content into written articles, blog posts, or social media posts. For example, you can transcribe an episode you heard on the radio and turn it into a blog post or social media update.
- Research: Recording interviews or focus group sessions can be a useful way to analyze qualitative data. For example, researchers can use Speechify to record interviews with study participants, which makes it easier to analyze and compare responses.
- Legal and medical documentation:Transcribe can be an important tool for legal and medical professionals who need to accurately document conversations. When doctors use Speechify to record notes from patients, the risk of miscommunication is reduced.
We've also recently been using it to generate subtitles forYouTube, where you can use Speechify on videos that are made entirely of voice and easily turn them into text that can be used as subtitles.
Component
The components that make up Speechify's Transcribe technology can be broadly categorized into areas of functionality and improvement.
Features:Automatic speech recognition (ASR) and speaker segmentation
- Automatic speech recognition to convert input audio to text
- How individual speech sounds are identified and combined into words and sentences.
- Can identify multiple speakers in recognized speech
- Features to provide more accurate transcripts for group conversations and interviews
Enhancements:Neural networks, NLP, error correction algorithms
- Neural networks trained on large datasets of spoken language to accurately recognize and transcribe speech.
- NLP that analyzes the context and meaning of words and phrases to create more accurate and meaningful transcripts
- Advanced error correction algorithms can identify and correct errors in your transcripts
- Recognize and automatically correct common errors like misspelled words, accents, and background noise
- Serves to improve the accuracy or relevance of speech during the TTS process
Conclusion: a good TTS depends on the accuracy of your dataset
As mentioned earlier, AI-powered speech processing requires large amounts of data. Speechify also uses models trained on large datasets of different accents, languages, speech styles, and spoken word data. This will include publicly available speech data as well as Speechify's proprietary datasets.
For example, open-source speech corpora like Mozilla's Common voice dataset can be used for speech processing. Audio recordings from news broadcasts, podcasts, and other sources can also be used as training data for speech processing.
Speechify's neural networks are fed a large and diverse sample of spoken language to learn the nuances and complexities of different accents, dialects, and speaking styles. We also collect and use proprietary datasets that are specifically designed to train our Speech to Text and Text to Speech models. By training our models with proprietary datasets, we've optimized them to work better with specific types of content or applications. As a result,Speechify is able to process data seamlessly, even from documents that contain abbreviations, numbers, and jargon, such as papers and news.
To summarize....
- Speechify uses NLP algorithms to analyze the text you enter.
- NLP is trained on large datasets of spoken language to analyze the context and meaning of words and phrases and create more accurate transcripts.
- It needs to be trained on large datasets to be able to process data smoothly, even from documents containing numbers or jargon, such as papers and news.
At its core, speech recognition training data is a set of audio files recorded by multiple speakers. It should also include spoken language sources such as call recordings, podcasts, and audiobooks to sample more accurate results. It's a much more complex data set because it's not just about language, it's about context, personality, and mood.
We are experts in building AI data and have been creating data that startups and enterprises can rely on. With our 99% accuracy data, we are confident that a global speech recognition platform that surpasses Speechify is just around the corner. For a sophisticated and differentiated data building strategy, DataHunt is here to help.
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