Sure!  Here's some example use cases that would demonstrate the breadth of benefits and drawbacks of natural language processing:

- **Optical character recognition** or **optical character reader** (**OCR**) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast). 
  - *Benefits of using NLP for OCR*
    - Very fast
    - Inexpensive
  - *Drawbacks of using NLP for OCR*
    - Not as accurate as human processing, such as crowdsourcing
    - Especially less accurate for nonstandard fonts, though workarounds exist and OCR can be trained to deal with particular fonts
- **Text-to-speech** (TTS), or **speech synthesis**, is transforming text into units that can be used to produce a spoken representation. Text-to-speech can be used to aid the visually impaired.
  - *Benefits of using NLP for TTS*
    - Arbitrary phrases can be spoken, instead of having to record voice actors for every possible combination of words
    - NLP produces more realistic speech and responds better to context than older non-NLP speech synthesis algorithms and circuits
  - *Drawbacks of using NLP for TTS*
    - Though they have improved dramatically in recent years, and exceed other kinds of automated TTS, most NLP TTS systems still have a slight inhuman quality to them, making them easily recognizable as automated
    - They do not have full range of expression of a human voice actor, and don't always guess the contextual clues for expression as well as humans
    - Partly due to both of these, some humans still prefer human voices to automated systems, regardless of helpfulness and accuracy, such as on customer service calls or voice acting in video games, despite the otherwise clear advantage of arbitrary speech generation (e.g. having characters speak a players name in a video game would be preferable to unnamed players, but would prohibitive to do with voice actors)
- **Speech recognition** is determining the textual representation of the speech, given a sound clip or live recording of a person or people speaking. It is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as **automatic speech recognition** (**ASR**), **computer speech recognition** or **speech-to-text** (**STT**). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. It is the reverse process of speech synthesis or TTS. 
  - *Benefits of using NLP for STT*
    - Much less expensive than human transcription services
    - Faster than most humans (though about on par with professional stenographers and closed captioning specialists)
    - Can recognize most words, and arbitrary phrases with reasonable accuracy, as opposed to traditional fixed pattern recognition STT, which had vocabularies limited to a handful of key words and phrases
    - Can recognize most human speakers, compared to older STT systems--even learning dictation systems--which could only recognize the users the system was trained on
    - Likewise, many NLP systems are now pretrained out-of-the-box, doing away with the often-hours-long training of these older dictation systems.
  - *Drawbacks of using NLP for STT*
    - In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary complex subtask of speech recognition 
    - In most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process 
    - Given that words in the same language are spoken by people with different accents, the speech recognition software must be able to recognize the wide variety of input as being identical to each other in terms of its textual equivalent 
    - Accuracy varies strongly with adverse audio conditions, and while this can be an issue for human interpreters as well, it is far more challenging for a STT system; often NLP alone is not enough and special compensation, such as multiple microphones is necessary to maintain quality
    - Error rates grow as vocabulary grows, so even though it compares favorably to non-NLP STT, for it is not suitable for large technical vocabularies in critical applications, such as space program communications
    - While pretraining is an advantage in some ways, it also means modern NLP STT systems take significant computing resources to train, and specialized personnel to supervise the delicate configuration of the training parameters; simpler STT systems, while less versatile, do not have this issue, and many have been in production for a long time
    - Special care must be taken regarding security when attaching a high quality NLP STT interpreter to a live computer interface, as they are sensitive enough to translate cyberattacks that are nearly inaudible to humans but easily picked up by an NLP STT, especially one with advanced audio to counteract adverse environmental conditions; this has been demonstrated in Amazon Alexa, for example
- **Sentiment analysis** (SA), also known as **opinion mining** or **emotion AI**, is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.
  - *Benefits of using NLP for SA*
    - Less expensive than hiring human labelers, and often hundreds of labelers are required for the sizes of datasets common in industries using SA
    - Often just as accurate as human labelers, as humans are also not very good at this task (80% accuracy on average)
    - Saves humans the distress of reading large volumes of disturbing content in order to label it
    - Performs much better at interpreting the subtle context found in reviews and internet posts than non-NLP SA systems, such as decision trees
  - *Drawbacks of using NLP for STT*
    - NLP is typically not explainable AI, so it cannot explain or justify its reason for a given assessment like a human assessor or a decision tree can
    - Also, because of this, NLP SA will make very different errors than human assessors, and thus accuracy figures are not entirely comparable; this makes both evaluating the models, and cooperation with humans, more difficult
    - Likewise, lack of explainability makes it high risk for bias, and makes bias more difficult to identify and remove; this necessitates intense preprocessing of data and careful ethical system design
    - Because evaluation of SA is becoming more and more task based in the industry, each implementation now needs a separate training model to get a more accurate representation of sentiment for a given data set; these extra training models take more computing and human resources to train than ever before
- **Automatic summarization** (**AS**), also known as **text summarization**, is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Often used to provide summaries of the text of a known type, such as research papers, articles in the financial section of a newspaper.
  - *Benefits of using NLP for AS*
    - Performs much better, with greater meaning retention and readability, than older statistical extraction/deletion methods
    - Less expensive and time consuming than human editors
    - Fast enough to perform summaries on the fly during document editing
    - Versatile enough to be combined with other NLP applications, such as OCR or STT; for example, to summarize a video broadcast by reading the closed captioning and/or transcribing the audio
  - *Drawbacks of using NLP for AS*
    - Training and evaluation requires reference texts, which uncommon and often only available from human editors; thus, for challenging domains that cannot use a general model and require a new model be trained specifically for the task, it can be difficult, expensive, and time consuming as simply using only human editors from the start
- **Machine translation** (**MT**) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. 
  - *Benefits of using NLP for MT*
    - Much less expensive and more available that human translators
    - Outperforms performs mechanical substitution, as that alone rarely produces a good translation in conversational texts; NLP recognizes whole phrases and their closest counterparts in the target language, handling cases such as not all words in one language having equivalent words in another language, and many words have more than one meaning
  - *Drawbacks of using NLP for MT*
    - Performs poorly on texts that require common sense-like semantic language processing or deep context
    - Inability to translate some non-standard language (slang, colloquialisms, idioms)
    - Difficulty identifying named-entities

I hope that helps.  Do you have any questions?
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