Our FAQ section explores how speech technology and artificial intelligence transform multimedia communication. Learn about speech-to-text, text-to-speech, and AI-powered subtitling, including their accuracy and limitations. Discover which languages and content types are best suited for AI-generated voices. MediaLocate‘s FAQs emphasize the essential blend of human expertise and machine efficiency in modern communication solutions.

Q: How does speech-to-text work?

A: Speech-to-text, called speech recognition or automated transcription, converts speech into written text using a computer. Until recently, transcription has been performed exclusively by trained transcriptionists listening to audio recordings or live speech and representing spoken words, phrases, and other vocalizations in a written format. With automated transcription, many methods exist, though most typically use an algorithm to map frequencies of the human voice into phonemes or other “units,” which are then constructed into words using either statistical or neural modeling. Nowadays, automated transcription can be nearly as accurate as humans, mainly on high-quality, clearly dictated speech. 

Q: How accurate is automated transcription?

A: The accuracy can vary widely depending on several factors, including the quality of the audio input, the language’s complexity, the speaker’s diction and accent, the presence of background noise, and the sophistication of the underlying algorithms.

Under optimal conditions, general-purpose speech recognition systems like those found in smartphones or virtual assistants can be pretty accurate. Modern speech recognition systems can achieve accuracy rates well above 90% in ideal environments with clear audio and standard accents.

However, the accuracy can decrease significantly in scenarios with background noise, varying accents, or poor audio quality. Accents, dialects, and variations in pronunciation can pose challenges for speech recognition systems, especially if they haven’t been extensively trained on diverse datasets. Additionally, languages with more complex phonetic structures or a wide range of vocabulary may be more challenging for speech recognition systems to transcribe accurately.

Leading-edge algorithms use neural network architectures capable of modeling large amounts of complex patterns in speech. 

Q: Why is human editing still needed?

A: While automated transcription technologies have advanced significantly, the nuanced skills of human editors remain indispensable for several reasons:

Contextual Understanding
Human reviewers deeply understand cultural nuances, idiomatic expressions, and context, allowing them to interpret and convey meanings accurately. Automated transcription systems may struggle with contextual understanding.

Domain-Specific Knowledge
In specialized fields like legal, medical, or technical translation, human reviewers possess domain-specific knowledge that automated transcription systems may lack. This expertise is crucial for accurate and precise translations.

Quality Assurance
Human reviewers ensure the overall quality of the translation by checking for errors, inconsistencies, and linguistic nuances that an automated transcription system might miss. They can refine the output to meet the desired standards.

Handling Ambiguities
When faced with ambiguous words or phrases, human reviewers can use their judgment, experience, and knowledge to choose the most appropriate interpretation. Automated transcription systems may struggle with ambiguity.

Post-Editing
Many professional transcription workflows involve post-editing of automated transcription output. Human reviewers can enhance and refine the machine-generated transcription to ensure accuracy and fluency.

Customization for Specific Needs
Organizations often have specific formatting or style preferences unique to their industry or brand. Human reviewers can tailor transcriptions to meet these particular needs.

Q: What is AI-powered Subtitling?

A: Video captioning systems follow a multi-step process to generate captions automatically. Firstly, the system dives into the video’s audio content through audio analysis. Here, speech recognition algorithms take center stage, transforming spoken words into text. The audio signal is meticulously processed to extract specific features to achieve this. These features, like spectrograms or MFCCs, are fed into the speech recognition model. Next, the speech recognition model takes the reins, transcribing the audio content into text. This step involves identifying and accurately transcribing the spoken words, all while considering various factors that can make things tricky. Examples include accents, background noise, and even identifying speakers if more than one person is talking (diarization). Deep learning models, like recurrent neural networks (RNNs) or convolutional neural networks (CNNs), are often the powerhouses behind this speech recognition task. The magic doesn’t stop after the audio is transcribed into text. Text processing steps in to further refine the raw text and ensure readability and accuracy. This might involve normalizing the text, correcting punctuation and grammar, and even identifying proper nouns and specialized terminology to ensure everything is crystal clear. Once the text is polished, it’s time for alignment. Here, the transcribed text is meticulously matched with corresponding timestamps in the video. Each caption or subtitle is linked to a specific time interval during which it should be displayed on the screen. This alignment ensures the subtitles perfectly sync with the audio and video content. To elevate the quality further, language modeling techniques can be applied. These techniques predict the most likely sequence of words based on the surrounding text, ensuring the captions flow smoothly and make grammatical sense. Finally, the generated captions undergo post-processing for a final touch-up. This might involve spell-checking, punctuation adjustments, formatting tweaks, and ensuring stylistic consistency. After all these steps, the quality of the generated captions is assessed using various metrics. These metrics measure accuracy, readability, synchronization with the audio and video, and adherence to language and style guidelines. In some cases, human reviewers might provide an extra layer of quality assurance and fine-tune the captions to meet the desired standards.

Q: How does text-to-speech work?

A: Text-to-speech, speech synthesis, or AI-generated voice generates speech based on an input text. Traditionally, speech has been recorded for media, such as videos, presentations, and films, by voice actors with desirable voice qualities trained to read and perform in various styles.  AI-generated voices use algorithms to generate recorded speech that mimics a human recording.

There are several steps to this:

Text Processing
The process begins with inputting written text, which may be typed text, documents, web pages, or any other textual format.

Linguistic Analysis
The text undergoes linguistic analysis to understand its structure, including identifying words, sentences, punctuation, and grammatical constructs. This analysis may involve tokenization, part-of-speech tagging, syntactic parsing, and other natural language processing (NLP) techniques.

Text Normalization
Text normalization involves standardizing the text to ensure consistency in pronunciation. This may include expanding abbreviations, converting numbers into spoken form, handling punctuation, and applying language-specific rules for pronunciation.

Text-to-Phoneme Conversion 
The processed text is then converted into phonemes, the smallest sound units of spoken language. Each language has its phonemes, and TTS systems use dictionaries or rule-based algorithms to map words to their corresponding phonetic representations.

Prosody Generation
Prosody refers to spoken language’s rhythm, intonation, and stress patterns. TTS systems generate prosody by applying rules or statistical models to determine pitch, duration, and emphasis for each phoneme or syllable in the synthesized speech. Prosody is crucial for conveying meaning, emotion, and emphasis in spoken language.

Waveform Synthesis
Once the phonetic and prosodic features have been determined, the TTS system synthesizes the speech waveform corresponding to the input text. There are several techniques for waveform synthesis, including concatenative synthesis, where pre-recorded speech segments are concatenated to form the output, and parametric synthesis, where speech waveforms are generated using mathematical models based on the phonetic and prosodic features.

Post-processing
The synthesized speech waveform may undergo post-processing to enhance its quality and naturalness. This may involve smoothing transitions between speech segments, adjusting pitch and timing, and adding additional effects to improve clarity and expressiveness.

Output
Finally, the synthesized speech waveform is outputted as audio, which can be played through speakers, headphones, or integrated into other applications and devices. The output speech closely resembles natural human speech, allowing users to interact with text-based content in a more accessible and intuitive manner.

Q: Which languages work best for AI-generated voice?

A: English remains the language with the most development in this field. 

Q: What content works best for AI-generated voice?

A: Since nuanced performances are still tricky to achieve with AI-generated voice, we recommend using them for content with a relatively straightforward and informative presentation style. 

• Training content
• Presentations
• Tutorials
• Shortform Clips

Conclusion